With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024

With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024 Gergely Ferenc Lendvai, Zsolt Kohus This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8745328/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines whether expansion in journal publication volume translates into higher prestige within the social sciences, a field where citation cycles are long and hierarchies persistent. Drawing on Derek de Solla Price’s insights into growth, the analysis problematizes the assumption that “more is better” by asking how volumes, citations, and prestige metrics evolved across quartiles (between 2004–2024), whether short-term increases in output yield contemporaneous gains in citations, SJR scores, or quartile mobility, and to what extent historical performance mediates these relationships. Using SCImago data, the study employs descriptive trend analysis, correlation tests, regression models, Markov chains, and clustering to try and answer the above questions in the context of structural and dynamic publication patterns. Results show steep stratification. Q1 journals doubled output and gained over 500% in citations, while Q4 stagnated in volume despite growing in number. Short-term volume increases had minor effects on prestige metrics, with correlations near zero and high quartile inertia, namely, that Q1 journals retained rank 84% of the time, Q4 remained stuck 61%. Historical SJR scores and citations may be referenced to predict future prestige while volume growth offered only modest and often asymmetric benefits which supported upward mobility mainly for lower-tier journals. journal prestige scientometrics bibliometrics publication volume social sciences cumulative advantage path dependence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In Little Science, Big Science , Derek J. de Solla Price ( 1963 ) – one of the most influential figures in scientometrics and science measurement (Danesh & Mardani-Nejad, 2020 ; Garfield, 2009 ; Dusek, 2021 ) – offered an observation that did not originate in a statistical laboratory, but rather in the quiet company of bound volumes of old scientific journals. Trained as a physicist and later turning his attention to the history of science, as anecdotally presented by Wang and Barabasi (2021), Price kept the issues of Philosophical Transactions by his bedside table, reading every issue meticulously. Price sorted each volume by year and began to notice a revelational occurrence – each year the piles of issues grew. He then began by counting; page by page, volume by volume the number of volumes and papers published over the 28 piles that had seemed to form an exponential growth curve (Wang & Barabasi, 2021). What he noticed was startling in its regularity – the total number of titles seemed to grow with uncanny consistency and when he plotted these counts on semi-logarithmic paper, the points aligned into a straight line: the unmistakable signature of exponential growth. To Price, this was not merely a numerical curiosity. Much rather, it suggested that science behaved like an organism to stick with Wang and Barabasi’s (2021) bacteria example regarding the sustainability of exponential growth whereas the science “produced” was expanding in bursts and sustaining itself through an ever-growing network of communication. This realisation, grounded in the simple act of tallying old pages, became one of the earliest empirical portraits of science as a self-reinforcing social system, and it remains a touchstone in bibliometric thought. Over the past six decades, it can be generally stated that academic knowledge production has undergone radical changes that Price could scarcely have imagined. The listing of all perimeters pertaining to this change could fill up dozens of monographs, however, exemplary or “disruptive” events can be traced back to the digitization of scholarly content, the rise of online submission systems, the proliferation of open-access and mega-journals, the rise of predatory outlets and unethical publishing practices that prioritize quantity over quality by all means, and the globalisation of scholarly authorship have reshaped the production and circulation of academic work. These developments have also removed many material barriers to increasing output, allowing journals to scale their publication volumes far beyond the constraints of print-era page budgets and even “opened up” science to traditionally marginalised regions – though it remains largely contested how successful this scope-widening was (see Demeter, 2020 ). Yet, despite this apparent abundance, academic prestige remains unevenly distributed and remarkably resistant to rapid change. This polemic can be substantiated by a plethora of factors. Historically, it can be said that prestige in scholarly publishing is both symbolically and materially consequential (Merton, 1968; Bourdieu, 1984 ; 1986; Burris, 2004 ; De Cruz, 2018 ; Singh, 2022 ; Demeter, 2020 ; Kohus et al., 2022 ; Lendvai et al., 2025 ; Morales et al., 2021 ). In practice, this could be translated to a rather simple assumption where high-prestige journals attract more and higher-quality submissions, secure greater visibility in citation databases, and influence research agendas within and beyond their disciplines (see Adams, 2013 ; Kwiek, 2020 ; Demeter et al., 2022 , 2025 ; Langin, 2019 ). Naturally, the field of scientometrics have moved way beyond this general observation. For instance, in the contemporary bibliometric ecosystem, prestige is often operationalised through complex quantitative composite indicators such as the quartile assigning which weights citations by the influence of the citing sources and classifies journals into quartiles (Q1–Q4) within their subject categories (Mañana-Rodríguez, 2014 ). Though quartile assignments differ in different repositories, mostly because they use different metrics to assign them (see Okagbue et al., 2020 ), in general, quartile rankings function as shorthand hierarchies (see Kosyakov & Pislyakov, 2024 ; Mason & Singh, 2022 ; Black et al., 2017 ). While the use of quartiles to rank journals are heavily criticized, Q1 journals are seen as leaders and gatekeepers, Q4 journals as marginal or peripheral (Seglen, 1997 ; Vȋiu & Păunescu, 2021 ; García et al., 2021; Liu et al., 2015 ; Köhler et al., 2020 ). Although quartiles are relative measures that shift with changes in the broader distribution, the movement of individual journals between quartiles tends to be slow and uneven, reflecting underlying structural inertia (Garfield, 2006 ; Vȋiu & Păunescu, 2021 ; Seglen, 1997 ; Kosyakov & Pislyakov, 2024 ). In the present paper, two theoretical perspectives are used to illustrate and explain this inertia. The first is cumulative advantage, commonly known as the “Matthew effect” (Merton, 1968), which predicts that journals already occupying prestigious positions are better able to attract submissions, generate citations, and maintain or increase their impact. The second is path dependence, which holds that initial conditions – such as historical citation patterns or long-standing editorial reputations – exert a continuing influence, making large positional shifts rare. Such path dependence can be seen in preferential attachment and citation concentration (Kozlowski et al., 2023 ), editorial bias and influence (Xie et al., 2019 ; Kulal et al., 2025 ) among many other aspects of publishing. All in all, these perspectives suggest that the quartile hierarchy may be self-reinforcing, with mobility constrained for both top and bottom tiers. Interconnecting Price’s scholarship and the above brief lamentation on prestige, against the above backdrop, one might rightly ask: does increasing a journal’s publication volume offer a viable path to higher prestige? The intuitive answer is mixed, even more so, since the complexity of academic publishing and its economic aspects play a crucial part in assessing the above query (Puehringer et al., 2021 ; Kwiek, 2020 ; Trueblood et al., 2025 ; Seglen, 1997 ; Brembs, 2018 ; Goel & Faria, 2007 ). On the one hand, publishing more articles increases the surface area for potential citations, raises journal visibility, and may signal vitality or growth to authors and readers (Drivas & Kremmydas, 2020 ; Zhang, 2020 ; Chen, 2024 ). On the other hand, expansion is “risky” (Zhang, 2020 ) and can dilute selectivity, potentially reducing average article quality and citation potential. Moreover, the mechanics of citation accumulation (or the SJR score calculation as presented later in this paper) mean that benefits from increased volume are unlikely to appear immediately; prestige metrics are often “lagging indicators,” sensitive to sustained patterns rather than short-term spikes. Despite the strategic and theoretical relevance of this question, existing evidence is scarce and fragmented. Some studies have documented broad increases in publication volumes across fields, regions, and publishers (Kim, 2025 ; Thelwall & Sud, 2022 ; Csomós & Farkas, 2022 ) but few have linked these changes directly to subsequent shifts in prestige. Analyses that do address this link often focus on either short-term correlations or long-term averages, without integrating both perspectives and even fewer studies consider how the effect of volume growth might differ across the quartile spectrum, potentially privileging Q1 journals with strong brand effects while offering little to Q4 journals whose outputs circulate on the periphery of citation networks. Problematizing volume in the context of impact is also critical in understanding the ever-growing number of scholarly literatures. Each year, the number of publications grow excessively (Thelwall, 2022 ; Bornmann et al., 2021 ), however, the impact of this volume growth or the strategic patterns in how impact correlates with growth is still a developing field of scientometrics. In the present endeavor, the focus lies on one particular field, the social sciences (SS). The SS provide a particularly compelling arena for exploring these issues since they comprise a heterogeneous mix of disciplines from economics and psychology to sociology and education each with distinct authorship patterns, topical dynamics, and norms around publishing frequency. Unlike many STEM fields, where rapid methodological innovation can drive swift changes in journal impact, social science fields often exhibit slower-moving citation patterns and longer time horizons for prestige accumulation as well as vastly different venue preferences (see Savage & Olejniczak, 2022 ). This makes it possible to observe structural tendencies – such as stratification or stability – over extended periods without the distortions caused by sudden, field-wide paradigm shifts. The present study builds on Price’s foundational insight by examining how growth in journal output interacts with the stratified prestige structure of the social sciences. It approaches the question from both a structural and a dynamic perspective: structurally, by mapping the evolution of volumes, citations, and SCImago Journal Ranking scores (hereinafter referred to as: SJR score) across quartiles over two decades; dynamically, by assessing whether changes in volume predict short-term or long-term shifts in prestige, and whether these effects are uniform across the quartile hierarchy. Furthermore, it also seeks to illuminate not only the mechanics of prestige accumulation but also the extent to which expansion strategies can disrupt – or reinforce – the existing hierarchy. Rather than treating short-term analyses as a test of “instant payoff,” the study conceptualises them as a test of prestige inertia under metric and citation-lag constraints. Lastly, we underline that in the present paper, we do not treat short-term analyses as a test of “instant payoff.” On the contratry, our study conceptualises them as a test of prestige inertia under metric and citation-lag constraints. Three research questions (RQ) guide the analysis. RQ1 How have publication volumes, citation counts, and SJR scores evolved across quartiles (Q1–Q4) in social sciences journals between 2004 and 2024, and what do these trajectories reveal about stratification in journal prestige? The primary purpose of RQ1 is to establish the empirical baseline of the study by mapping long-term trends in output, citations, and prestige across quartiles. Rather than merely documenting overall growth, this question examines how expansion and impact have been distributed within the prestige hierarchy. RQ1 assesses whether growth has been broadly shared or disproportionately concentrated among higher-ranked journals, thereby providing the structural context for the subsequent analyses. RQ2 To what extent do annual changes in publication volume coincide with changes in citations, SJR scores, and quartile positions, given the lagged nature of citation accumulation and the construction of prestige metrics? RQ2 shifts the analytical focus from long-term trajectories to short-term dynamics. Instead of treating annual volume expansion as a strategy expected to yield immediate prestige gains, this question examines whether prestige indicators exhibit responsiveness or inertia in the short run. Subsequently, RQ2 complements RQ1 by testing whether the stratified structure identified at the macro level is reflected in limited short-term mobility, or whether journals experience measurable contemporaneous shifts in prestige following changes in output. RQ3 How strongly does historical journal standing (measured by prior SJR scores and citation levels) condition subsequent prestige outcomes relative to contemporaneous changes in publication volume, and do these relationships differ across quartiles? RQ3 integrates the structural patterns identified in RQ1 with the short-term dynamics examined in RQ2 by explicitly foregrounding the role of accumulated advantage. It investigates whether historical prestige dominates subsequent outcomes and conditions the association between volume growth and changes in prestige indicators. The goal of RQ3 is to clarify whether expansion operates differently depending on a journal’s initial position in the hierarchy and helps explain why long-term divergence may persist despite weak short-term responsiveness. Via situating these questions within the dual frameworks of cumulative advantage and path dependence, the study addresses a central tension in scholarly publishing: whether expansion in scale can serve as a mechanism for upward mobility in a prestige hierarchy that appears resistant to change. The findings have implications for editors deciding whether to scale their journals, for publishers balancing commercial and reputational goals, and for policy-makers whose reliance on quartile rankings influences the incentives that shape scholarly behaviour. More broadly, they contribute to ongoing debates about the nature of growth in science, suggesting that while the volume of published work continues to rise, the benefits of this expansion may accrue disproportionately to those already at the top. 2. Materials and methods The dataset for this study was drawn from the publicly available SCImago Journal & Country Rank (or SJR) portal ( https://www.scimagojr.com/ ) , one of the most renowned and utilized journal repositories which compiles bibliometric indicators derived from Elsevier’s Scopus database (González-Pereira et al., 2010 ; Jacsó, 2010 ). SCImago provides a comprehensive set of annual metrics for indexed journals, including total documents published, total citations received, the SJR score, and quartile classification within subject categories (González-Pereira et al., 2010 ; Falagas et al., 2008 ; Jacsó, 2010 ). The journal selection was conducted as follows. First, only journals assigned to the Social Sciences subject area were included, all other publication venues (book chapters, conference proceedings) were excluded. This subject area is one of the largest in the SCImago system with 24 distinct subject categories ranging from anthropology to law to urban studies (Sasvári & Lendvai, 2024 ). Although the overall period spans 21 years, with 2024 used as a reference for 2025, the primary analysis focuses on a 20-year period from 2005 to 2024. For the forecast for 2005, the year 2004 was also included as a necessary reference point. Data was collected on 1 August 2025. A critical limitation shall be mentioned in terms of data employed. SCImago, though builds on Scopus’s system, does use a slightly different categorization system. Therefore, the 24 subject categories are handled under one “branch” in the framework where discipline-specific differences are to be evaluated journal-by-journal or by subject categories. Since we handled all subject categories together, we did not differentiate between different disciplines or fields when evaluating the results. Nonetheless, we do encourage future research endeavors to explore the disciplinary differences on a micro-scale as well, since our study solely provides the “big picture” on social sciences. 2.1. Data extraction and preparation The full list of journals in the Social Sciences subject area was downloaded from SCImago for each year between 2004 and 2024. For each journal-year observation, all variables were extracted, however, we filtered indicators selectively in accordance with our research scope: The indicators included were the following: Year – the calendar year of the metric. Quartile – Q1, Q2, Q3, or Q4 classification within the journal’s primary SCImago subject category. In some cases, SCImago includes journals without a quartile assignment (these are indicated with a “-“ in the dataset). We only included these journals where quartile examination was not present. In the dataset, for each journal in each year a Best Q is assigned. This means that in the particular year the respective journal is assigned a Q or maybe Qs in multiple subject categories, however, since we worked with the “umbrella” subject area, Best Q signifies that a journal has achieved its best Q ranking in terms of all Qs assigned in the subject categories it is categorized in. In more practical terms, if XY journal is assigned four different Qs in four subject categories under the Social Sciences subject area (X – Q1, Y – Q2, Z, - Q3, A – Q2), the Best Q will be Q1 since it is the best Q it has achieved. This, as mentioned above, only means that we analyzed the holistic landscape and did not take into account the differences in specific subject categories (the statistics of journals in SCImago with missing Qs can be found under Appendix 2). Total documents – the total number of citable documents (articles, reviews, conference papers) published in that year. Total citations – the total citations received by the journal in that year. For this, Total_Cites (3y) was used which signifies the total citations received in the last 3 years of the respective index-year. This also happens to be the main citation metric used by SCImago Journal Rank. SJR score – the SCImago Journal Rank value for that year (González-Pereira et al., 2010 ). The SJR score (sometimes referred to as “indicator”) is a sophisticated metric that is defined by González-Pereira et al. ( 2010 ) as a “size-independent metric aimed at measuring the current “average prestige per paper” of journals for use in research evaluation processes.” The SJR score is based on multiple metrics such as citation weighting schemes, citation network (including centrality of the citations), in order to indicate where the journal in the respective discipline was in terms of relevancy and impact (Sasvari & Lendvai, 2024; González-Pereira et al., 2010 ). Though the uniformization of impact assessment of SJR scores would not be adequate, per Sasvari and Lendvai (2024), a general rule of thumb is that if an SJR score is higher than or equivalent to 1.00, it might be correct to assume that the respective journal is impactful (or elite, in more qualitative terms), while journals with an SJR score of less than 1.00 might be considered less impactful in terms of purely quantitative terms. Journal identifier – a unique string to track journals over time. We used this to effectively identify individual journals for our analyses. All years in which a journal was absent from SCImago’s Social Sciences listings (due to indexing gaps, category reassignments, or inactivity) were recorded as missing and treated according to the procedures described below. 2.2. Variable transformations Three types of measures were derived from the raw indicators: For descriptive trend analysis, annual mean and median values of total documents, total citations, and SJR score were calculated for each quartile (mean and median values per quartile-year). This enabled the comparison of central tendency measures and the detection of skewness (e.g., whether mean values were inflated by a small number of extreme journals). Since we were interested in percentage changes (%Δ), a Year-to-year percentage change was computed for each journal’s total documents, total citations, and SJR score. For example, the percentage change in total documents for journal i between years t and t + 1 was calculated as: $$\:\%\:\varDelta\:\:\text{docs}\:=\:\frac{{\text{docs}}_{t+1}\:-\:{\text{docs}}_{t}}{{\text{docs}}_{t}}\:\times\:\:100$$ This allowed the analysis of short-term fluctuations independent of absolute size differences. Lastly, annual differences (Δ) were also computed, especially, for some models (e.g., change in SJR score) as absolute differences rather than percentage changes were used to capture directional movement in prestige metrics. To preserve longitudinal continuity in the analysis, a gap-tolerant lag–lead structure was used. This means that where a journal was missing in one or more intermediate years but reappeared later, its next available year was paired with its most recent prior year to compute changes. This avoided the common bias of dropping journals with incomplete annual coverage and allowed the inclusion of longer trajectories. 2.3. Analytical strategy The study combined descriptive statistics, association tests, regression modelling, Markov chain analysis, and cluster analysis to examine the relationships between publication volume and prestige from multiple angles. Each method addressed a different dimension of the research questions. For each quartile (Q1–Q4), annual means and medians of total documents, total citations, and SJR scores were plotted over the 2004–2024 period. Comparing mean and median curves allowed identification of skewness, for example, whether quartile-level growth was driven by a subset of high-volume, high-impact journals. Linear trend slopes were calculated for each indicator by quartile, and compound annual growth rates were computed to quantify percentage growth over the full period. Pearson correlations were computed between %Δ documents and %Δ citations, and between %Δ documents and Δ SJR score, to assess linear relationships between short-term output changes and prestige changes. Spearman rank correlations were calculated between %Δ documents and quartile improvement (binary indicator of whether the journal moved to a higher quartile in the following year) to capture monotonic but potentially non-linear associations. We applied three regression models, too to sophisticate our specialized examinations. We employed the following models: Ordinal logistic regression (VGAM package in R) was used to predict quartile rank (Q1–Q4) in year t + 1 from %Δ documents, prior SJR scores, and prior citations in year t. Odds ratios were reported to indicate the relative effect sizes (RQ1 and RQ2). Linear regression models predicted Δ SJR scores and %Δ citations in year t + 1 from %Δ documents, prior quartile, prior SJR, and prior citations. Interaction terms between %Δ documents and prior quartile were included to test whether the effect of expansion differed by starting position (see RQ2). Generalised additive models (GAMs) were estimated to explore non-linear relationships between %Δ documents and prestige changes, with smooth terms fitted for prior SJR scores and prior citations (RQ3). Lastly, for RQ3, a Markov chain, a 4×4 transition probability matrix was constructed for quartile mobility, showing the likelihood of moving from quartile i in year t to quartile j in year t + 1 . This was computed for the full dataset, for journals above the median %Δ documents (high-growth group), and for those below the median (low-growth group). Probabilities were compared across groups to assess whether higher volume growth was associated with greater upward mobility. To add rigor to the above analyses, three robustness checks were conducted to ensure stability of the results using a Winsorised model based on extreme %Δ document values at the 1st and 99th percentiles to limit outlier influence. 2.4. Cluster analysis Lastly, for the cluster analysis with interconnection to RQ3, a hierarchical clustering (Ward.D2 linkage) was applied to standardised longitudinal indicators, including median and interquartile range of %Δ documents and %Δ citations, median and IQR of Δ SJR, quartile improvement probability, document and SJR score slopes, and Kendall correlation between documents and SJR. The optimal number of clusters ( k ) was determined by silhouette width analysis, and principal components analysis (PCA) was used for visualisation. Clusters were profiled in terms of stability, volatility, and output–prestige coupling. The results are explotary in nature and solely serve the function to invite future scholarship to reflect in clustering mechanisms when studying prestige and scholarly output connections. We report the results in Appendix 3. 3. Results 3.1 General results on the change of volume and “prestige metrics” (RQ1) Between 2004 and 2024, the analysis of the examined journals shows clear upward trends in journal output, citation counts, and SJR scores, with notable differences between mean and median values. Mean citation counts rose sharply from around 54 in 2004 to over 350 in 2024 which is a substantial increase in aggregate citation volumes. However, median citations increased more modestly from 15 to 61 reflecting that the influence is more accentuated in the case of a small number of highly cited journals. Mean journal volumes grew steadily from about 28 to over 50 papers per year, whereas median volumes remained relatively stable around 22–27. Regarding SJR, the mean value increased from 0.38 to a peak above 0.50 in the mid-2010s before stabilising, while the median SJR score rose from 0.19 to roughly 0.26, again showing that a small subset of journals skews the average upward. The disparity between mean and median across all three indicators also confirms the presence of strong right-skewness in the dataset, driven by elite journals. Statistical summaries further support this statement. For citations, the standard deviation of the mean trend (82.31) is more than seven times that of the median trend (11.75) which can be converted to an extreme variability among top performers. The R² values show that time explains a large share of the variance for average journal volume (0.886) and median citations (0.836), but less for average SJR score (0.354), implying weaker temporal predictability in journal prestige. The SJR score median trend’s R² of 0.657 still suggests a moderate, consistent upward movement in typical journal standing, however, its statistical significance is not strong. Across the period, median journal volume remained strikingly constant, which may indicate structural stability in most journals’ publishing capacity. In contrast, the rapid rise in mean citations from around 2016 onwards likely reflects the combined effects of larger publication volumes in certain outlets, shifts in citation practices, and perhaps increased internationalisation. (Fig. 1 , the full descriptive statistical results are available under Appendix 1) Turning onto quartiles, for Q1, mean volume rose from 38.53 in 2004 to 91.62 in 2024, an absolute gain of 53.09 and a 137.79% increase, with a linear slope of 2.1748 papers per year. Q1 mean citations climbed from 157.88 to 1015.14, a rise of 857.26 or 542.98%, on the steepest citation slope of 35.8411 per year. Q1 mean SJR score increased from 0.920 to 1.068, up by 0.148 or 16.07%, with an annual slope of 0.005122. The number of Q1 journals expanded from 863 to 2396, adding 1533 titles for a 177.64% increase. For Q2, mean volume moved from 29.56 to 43.95, up 14.39 or 48.70%, with a slope of 0.6128 per year. Q2 mean citations rose from 39.52 to 174.42, a gain of 134.89 or 341.29%, on a 6.2503-per-year slope. Q2 mean SJR score advanced from 0.261 to 0.360, up 0.099 or 37.85%, with a 0.003430 annual slope. Q2 titles increased from 802 to 2167, adding 1365 journals for a 170.20% rise. For Q3, mean volume grew from 23.40 to 31.52, a gain of 8.12 or 34.70%, with a 0.3169 slope. Q3 citations went from 14.80 to 65.43, increasing by 50.63 or 342.11%, on a 2.4598 annual slope. Q3 SJR score edged up from 0.148 to 0.199, a 0.051 change or 34.63%, with a 0.002330 slope. Q3 journal counts expanded from 739 to 1941, adding 1202 titles for 162.65% growth. For Q4, mean volume slipped from 21.33 to 20.53, a decrease of 0.80 or − 3.76%, despite a nominal positive slope of 0.0761. Q4 mean citations improved from 4.18 to 20.22, up 16.04 or 383.49%, on a 0.8283-per-year slope. Q4 mean SJR score climbed from 0.105 to 0.124, a 0.018 increase or 17.15%, with the smallest slope at 0.000832. Q4 journal counts still expanded from 689 to 1813, adding 1124 titles for 163.13% growth. The volume ratio of Q1 to Q4 widened from 1.81 in 2004 to 4.46 in 2024 which points to a strong top-tier expansion in output relative to the bottom tier. The citation ratio of Q1 to Q4 stretched from 37.74 in 2004 to 50.19 in 2024 indicating an even faster divergence in impact. By contrast, the Q1–Q4 SJR score ratio nudged slightly down from 8.72 to 8.64, suggesting small convergence in prestige despite persistent gaps. In 2024, Q2 citations were 2.67× Q3’s. This finding shows a clear separation in (upper) middle-tier impact. Slope comparisons further underscore stratification: Q1’s citation slope is 5.73× Q2’s and 43.27× Q4’s, while its volume slope is 28.57× Q4’s. On compound growth, Q1’s volume CAGR is 4.43% per year versus 2.00% for Q2, 1.50% for Q3, and − 0.19% for Q4. Citation CAGRs are high across the board − 9.75% (Q1), 7.71% (Q2), 7.72% (Q3), and 8.20% (Q4) – but absolute levels remain far apart. For SJR, annual compound gains are 0.75% (Q1), 1.62% (Q2), 1.50% (Q3), and 0.79% (Q4), consistent with slow-moving prestige metrics. Journal counts compound at roughly 5.24% (Q1), 5.10% (Q2), 4.95% (Q3), and 4.96% (Q4) per year, showing broad expansion in the number of outlets. The combination of steep citation growth and rising volume in Q1 signals compounding advantages in both reach and recognition. Meanwhile, Q2 and Q3 improve steadily but not fast enough to close the gap with Q1. Overall, 2004–2024 shows a publishing ecosystem where top-quartile journals compound both scale and impact, middle tiers make steady gains, and the lowest tier grows mainly in count but lags in output and influence. In more interpretative terms, the results point to an increasingly stratified scholarly publishing system in which Q1 journals consolidate dominance by expanding both their output and citation advantage at a much faster rate than all other quartiles. Although “mid-tier” journals, namely, Q2 and Q3 journals show “healthy” growth in citations and volume, their slower slopes mean they are unlikely to catch up with Q1 without structural changes in visibility, indexing, or funding. Q4 journals’ stagnation in volume alongside modest citation gains suggests that expansion in their numbers does not translate into greater influence, potentially reinforcing a perception of low-tier irrelevance. On a more critical note, the widening citation and volume gaps point toward a reinforcing cycle where prestige, resources, and impact are disproportionately concentrated in the top quartile, making upward mobility increasingly difficult for lower-ranked journals. (Fig. 2 ) 3.2. Changes and trends in the dyad of prestige and volume (RQ2) Since we wanted to understand the above stratification in a more comprehensive manner, we employed several analyses that examine the relational aspects of changes in volume and impact. First, the association analyses reveal that short-term changes in publication volume show almost no statistical relationship with immediate shifts in prestige metrics. The Pearson correlation between %Δ volume and %Δ citations is only 0.024, essentially indicating no linear association. This means that, on average, years when journals increase their output are not systematically accompanied by proportional changes in citation counts in the same year. Similarly, the Pearson correlation between %Δ volume and Δ SJR scores is only 0.013, reinforcing that SJR scores do not respond strongly to annual fluctuations in output. For SJR scores we used the absolute values and not percentages in this case, since it better represented the “actual” change in a more interpretable way. The Spearman correlation between %Δ volume and quartile improvement is slightly higher at 0.055, but still negligible and presents that volume growth is not a consistent driver of moving into a better quartile rank. These extremely low coefficients remain despite large sample sizes (over 96,000 year-to-year journal observations) indicating the effect is practically non-existent. The weak correlations imply that volume changes alone are not a reliable tactical lever for boosting short-term prestige. This fits with theoretical expectations that citations accrue with a lag, and that SJR score is resistant to volatility due to its network weighting. The slight positive link to quartile improvement might reflect cases where sustained growth over multiple years eventually pushes journals into a higher quartile, but such effects are washed out in a purely annual correlation. Taken together, the results argue for caution in interpreting volume increases as a quick path to higher impact metrics. They also show the importance of multi-year perspectives in editorial strategy, as short-term boosts in publication counts rarely move prestige indicators in the same period. (Fig. 3 ) 3.3. Historical performance and predictive analyses (RQ3) We then turned to predictive modelling to quantify how journal output growth relates to prestige shifts when looking at historical performance. We used the absolute values of SJR scores in this case as well. The ordinal logistic regression using VGAM predicted quartile rank (Q1–Q4) from percentage change in publication volume, previous SJR, and previous citations. Results show that all predictors were highly significant ( p < 0.0001). A one standard deviation increase in %Δ volume increased the odds of being in a higher quartile by 8.61% (OR = 1.086, 95% CI [1.073, 1.099]). Previous SJR score had the largest effect: a one standard deviation increases more than doubled the odds of a higher quartile (OR = 2.194, 95% CI [2.142, 2.247]) and prior citations also mattered, with an OR of 1.119 (95% CI [1.081, 1.158]). Translating these into simpler that, these results essentially mean that journals with more historical citations were modestly more likely to improve in quartile and confirm that baseline prestige indicators dominate quartile changes, but volume growth still provides a measurable, positive contribution. The linear regression for Δ SJR scores found a small but highly significant effect of %Δ volume ( β = 0.0205, p < 2.42×10⁻¹⁶). This means that, on average, a one standard deviation increase in volume change corresponds to a 0.0205-point increase in SJR considering other factors. Previous quartile had a small negative coefficient ( β = -0.0027, p = 0.055), suggesting a possible ceiling effect where higher-ranked journals gain less in SJR. Previous SJR scores had a strong negative relationship with Δ SJR scores ( β = -0.0538, p < 2×10⁻¹⁶), essentially reinforcing this ceiling effect where elite journals have less room to increase further. Prior citations had a small but significant positive association ( β = 0.0057, p < 1.06×10⁻⁸). The interaction term between volume growth and prior quartile was positive ( β = 0.0050, p = 0.00216), meaning that lower-quartile journals benefitted more in SJR score from increasing volume than higher-quartile ones. The linear regression for %Δ citations revealed that %Δ volume significantly predicted citation growth ( β = 2.286, p = 0.00037). This implies that a one standard deviation increase in volume change is associated with a 2.29 percentage-point increase in citation growth. Previous quartile was a strong positive predictor ( β = 13.226, p < 2×10⁻¹⁶), meaning higher-quartile journals tend to experience much larger percentage citation increases year-to-year. Surprisingly, previous SJR score was not significant ( p = 0.21) in this model, and prior citations had a marginally negative effect ( β = -0.919, p = 0.013). The interaction between volume growth and prior quartile was positive but only marginally significant (β = 1.403, p = 0.055), hinting that expansion may slightly amplify citation gains more for top-tier journals. Our third model was the GAM model for Δ SJR scores that showed that all three predictors (%Δ volume ( p < 2×10⁻¹⁶), previous SJR scores ( p < 2×10⁻¹⁶), and previous citations ( p < 2×10⁻¹⁶)) had significant non-linear effects. The smooth term for previous SJR score illustrated a steep decline in potential SJR score gains as baseline SJR score increased, confirming the ceiling effect from the linear model. The smooth term for %Δ volume showed that moderate increases in volume were associated with the largest consistent SJR score gains, while very high increases produced unstable or even negative returns. Prior citations displayed a hump-shaped pattern: benefits peaked at moderate historical citation counts before tapering off. The GAM model for %Δ citations also found a strong non-linear relationship for %Δ volume ( p < 2×10⁻¹⁶), where moderate expansion correlated with the largest proportional citation gains, but extreme increases could dampen citation growth. Previous quartile remained significant as a parametric term ( β = 12.817, p < 2×10⁻¹⁶), reinforcing the structural advantage of high-ranked journals. Prior SJR scores had a weaker but still significant smooth effect ( p = 0.012), while prior citations were not significant ( p = 0.064) after taking into account other factors. Taken together, the models show that previous SJR score is the most powerful driver of quartile position changes, dwarfing the effect of volume growth in magnitude. Citation history also matters, but its influence is smaller and more variable across models. Volume growth consistently predicts positive changes in prestige metrics, but its effect is modest and often strongest for journals starting from lower prestige positions. The interaction effects suggest an asymmetry. Lower-quartile journals gain more SJR score from expansion, while higher-quartile journals are better at turning expansion into citation growth. The GAM results, however, caution against assuming linear returns from growth; moderation often outperforms extreme expansion. The low adjusted R² values across models (generally under 5%) highlight that much of the variation in prestige changes remains unexplained, suggesting that other factors like editorial selectivity, topical novelty, or author network effects play major roles. As mentioned in the Introduction, these results substantiate what we call a “path-dependent prestige system”. Let us expand briefly in the context of the results of this analysis. By path-dependent prestige system, we mean that top journals tend to remain at the top, benefitting disproportionately from their historical metrics confirming Merton’s (1968) Matthew effect. Lower-tier journals can climb the prestige ladder through controlled, quality-focused expansion, but face structural limits imposed by entrenched hierarchies. For citation growth, high-tier journals leverage both their brand and scale more effectively than their lower-tier counterparts. Strategically, the optimal approach for growth depends on the starting position: elite journals can safely scale, while emerging journals may need to balance volume increases with sustained quality improvements to break into higher quartiles. (Fig. 4 ) Lastly, we examined whether short-term changes in publication volume are associated with subsequent changes in journal prestige indicators (specifically SJR scores and quartile positions) treating these analyses as tests of responsiveness versus structural inertia rather than predictive effects. The first step was the construction of a gap-tolerant lagged panel dataset, which ensured that each journal-year observation was linked to its most recent prior available year, even if the years were not consecutive. This avoided the common pitfall of losing data due to missing intermediate years (e.g. a journal is indexed as a Q4 journal in 2022, falls out in 2023, then gets back in 2024 as a Q3 journal) and allowed for the inclusion of all available temporal relationships. For each observation, “lagged” predictors were calculated as follows: percent change in publication volume, previous year’s SJR, previous year’s total citations, and previous year’s quartile rank. Correspondingly, “lead” outcome variables were calculated for the following year: change in SJR, percent change in citations, and next-year quartile rank. This lag-lead structure created a dataset suitable for panel econometric modeling and Markov chain transition analysis. First, two main panel regression models were estimated. The first predicted the change in SJR scores in year t + 1 as a function of percent change in volume in year t , checked with prior SJR score and prior citations. The second predicted the percentage change in citations in year t + 1 using the same predictors. Each model was run with both fixed-effects (FE) and random-effects (RE) specifications. The Hausman test was used to decide between FE and RE for each model, ensuring the treatment of unobserved heterogeneity was statistically sound. FE models controlled for time-invariant journal characteristics (e.g., disciplinary focus, indexing coverage), while RE models assumed that unobserved factors were uncorrelated with the predictors. In parallel, a Markov chain model was constructed to capture quartile mobility over time. For each journal-year observation, the quartile in the previously available year became the “from” state, and the quartile in the following year became the “to” state. This yielded a 4×4 transition probability matrix describing the likelihood of moving between Q1, Q2, Q3, and Q4 from one year to the next. The analysis was conducted three times: for the full dataset (overall), for high-growth journals (above the median percent change in volume), and for low-growth journals (below the median). This allowed for direct comparison of mobility patterns by growth profile. The base case transition matrix revealed a high degree of persistence at the quartile extremes. Q1 journals had an 84.2% probability of remaining in Q1 year-to-year, with a 14.3% probability of dropping to Q2 and less than 2% chance of dropping further. Q4 journals had a 61.0% probability of staying in Q4, with a 30.7% chance of moving up to Q3, 7.7% to Q2, and almost no chance (0.6%) of leaping to Q1. The middle quartiles were more dynamic. Q2 journals moved up to Q1 in 21.6% of cases and down to Q3 in 15.1%, while Q3 journals moved up to Q2 in 27.2%, remained in Q3 in 59.6%, and dropped to Q4 in 10.3%. Direct non-adjacent jumps (e.g., Q3→Q1) were rare, at only 2.9%. The panel regression results were notable for the extremely small coefficients on the volume growth variable. In the fixed-effects ΔSJR score model, the coefficient was 0.000089 which implies that even a very large increase in publication volume would be associated with a negligible change in SJR score in the following year. In practical terms, this means that increasing output alone without changes in citation quality or other impact-related factors is unlikely to move a journal into a higher quartile in the short term. To reiterate, strictly from a strategic perspective, this means that simply increasing publication volume (even substantially) is unlikely to produce rapid gains in quartile ranking. Other factors, such as improving citation impact, targeting higher-impact submissions, and enhancing journal visibility, are more likely to produce upward mobility. In sum, the integrated panel regression and Markov chain analysis – combined with targeted robustness checks – shows that short-term volume growth is not a significant driver of SJR score or quartile changes in the Social Sciences. The quartile mobility structure is highly stable, resistant to extreme cases, unaffected by the choice of growth metric, and robust to missing data. (Fig. 5 ) 4. Discussion 4.1. Summary of the findings The present study set out to examine the relationship between journal output growth and prestige in the social sciences over two decades, drawing on SCImago Journal Rank data from 2004–2024. We combined descriptive statistics, correlation tests, regression modelling, Markov chain analysis, and cluster profiling, in order for the analysis to assess both long-term trajectories and short-term dynamics to clarify whether expansion in volume offers journals a pathway to higher prestige or whether historical hierarchies dominate. The findings were interpreted through the dual frameworks of cumulative advantage and path dependence, both of which emphasize inertia and reinforcement in scholarly publishing systems. Regarding RQ1, on the evolution of volumes, citations, and prestige across quartiles, the data reveals a salient stratification. Q1 journals more than doubled their output, growing from an average of fewer than 40 papers per year to over 90, and their citation counts surged sharply (more than 500%). In contrast, Q2 and Q3 outlets expanded at more modest rates, while Q4 journals stagnated, even posting slight declines in average annual volume. Citation counts rose across all quartiles, but the increases were highly uneven, with top-tier journals pulling away from the rest. SJR scores moved upward incrementally across quartiles but without major structural change, confirming the slow-moving character of prestige metrics. The skewness between means and medians highlights that this expansion was driven by a handful of elite outlets rather than broad-based growth. Therefore, in short, the period examined witnessed an entrenchment of stratification, with leading journals consolidating dominance while lower-tier outlets remained marginal. Turning to RQ2, on short-term relationships between volume growth and contemporaneous changes in impact indicators, the results show that output expansion does not translate into immediate prestige. Correlations between annual changes in document counts and parallel changes in citations or SJR scores were essentially zero, even across nearly 100,000 observations. Median year-to-year changes were minimal which can be understood as strong systematic stability. Quartile mobility patterns reinforce this inertia: leading journals had more than an 80% chance of remaining at the top from one year to the next, while the lowest-ranked outlets had a 61% probability of staying at the bottom. Movement was largely confined to adjacent quartiles, and radical leaps almost never occurred. Even extreme increases in publication volume did not trigger contemporaneous shifts in ranking, underscoring the lagging nature of citation accrual and the resistance of quartile classifications to short-term fluctuations. Finally, as to RQ3, on the role of historical performance as a conditioning factor of future prestige, the evidence shows that prior standing exerts a far greater influence than changes in output. Previous SJR scores were by far the strongest predictors of quartile placement in subsequent years, with prior citation counts also significant but somewhat less influential. Volume growth contributed positively to regression models, but its effect was modest in comparison, and interaction terms revealed asymmetries. Associations were asymmetric. Volume increases were more strongly related to incremental SJR changes among lower-quartile journals, whereas higher-quartile journals showed larger associations between expansion and citation growth. To sum up the results considering the three RQs, the findings establish that the SS journal ecosystem is structurally stable and strongly hierarchical. Growth in output continues across the field but its benefits accrue disproportionately to journals already occupying prestigious positions (see Zhang, 2025). Moreover, expansion may support gradual progress for some outlets, particularly in the middle tiers, yet the overall system is resistant to disruption. Prestige in academic publishing thus remains less a function of short-term scaling and more the outcome of long-standing trajectories, confirming that in the competitive landscape of scholarly communication, history matters as much as present strategy. 4.2. Implications and understanding the results in a broader context As with most complex scientometric queries, the question posed in the title of the present paper ( With greater volume comes greater prestige? ) cannot be answered with a simple yes or no . The evidence presented here paints a much more compounded picture, one in which expansion of journal output does, indeed, play a role in prestige dynamics, but rarely in the direct, year-to-year manner implied by ‘instant payoff’ interpretations of prestige metrics. In brief, volume growth contributes, but always in the shadow of entrenched hierarchies, with history and accumulated advantage exerting the decisive influence. Subsequently, prestige, as the findings show, is not something that can be manufactured in the short term through aggressive scaling, but rather something deeply embedded in long-standing trajectories of citation, recognition, and reputation (cf. Zhang, 2025). From a theoretical standpoint, these results resonate strongly with Merton’s enigmatic formulation of the Matthew effect. Journals that already occupy prestigious positions, i.e. those at the top quartile, can leverage their visibility and accumulated capital to turn even modest growth into further gains. They also enjoy disproportionate benefits from expansion (Zhang, 2025) because each additional article is more likely to be cited, more likely to attract submissions from high-status scholars, and more likely to be disseminated through networks that reinforce their dominance. By contrast, lower-quartile journals may expand their volumes substantially, but the relative invisibility of their content and the lack of established citation streams mean that these efforts often translate into negligible changes in prestige metrics. Across models, historical standing (prior SJR and citations) dominates short-term changes in prestige indicators, consistent with cumulative advantage and path dependence. The empirical evidence confirms this inertia. Quartile mobility is overwhelmingly “sticky,” with Q1 journals maintaining their positions more than 80% of the time and Q4 journals trapped in place more than 60% of the time. Consequently, even when volume surges are dramatic, they do not dislodge the gravitational pull of history. A critical question, therefore, remains. Are quartiles good representations and is there a need for a change? The abovementioned asymmetry is central to interpreting the findings. For top-quartile journals, scaling output appears relatively “safe.” From a more critical standpoint, their brand prestige insulates them from the risks of dilution as readers and authors already assume a certain level of quality, and even if the acceptance rate widens, the symbolic value of being published in a Q1 journal remains high. Though previous research revealed journal quality perception gaps and varying opinions on journals prestige (Bryce et al., 2020 ; Oltheten et al., 2005 ; Lowe & Locke, 2005; Lowry et al., 2007 ; Brooks et al., 2021 ), it can be underlined that elite journals falling in the top quartile remain both symbolically and qualitatively elite in terms of scientometric and bibliometric measures. More importantly, these journals sit at the centre of citation networks, which magnifies the impact of additional articles and the potential of receiving higher quality papers (see Ujum et al., 2021 ; Wakefield, 2008 ; West et al., 2010 ; Zhang et al., 2011 ; McGuigan et al., 2021 ; Traag, 2021 ). Therefore, in effect, they can convert scale into visibility and citations with greater efficiency than any other quartile. For Q4 journals, however, the picture is bluntly different. Expansion not only fails to produce equivalent returns, but may even backfire by straining editorial quality, undermining selectivity, or flooding the market with articles that remain uncited (see, e.g. Malvić et al., 2022 ; Maddi & Miotti, 2024 ). It is, thus, claimable that the publication system punishes, or in more conservative terms, marginalizes lower-tier journals that attempt to emulate the strategies of their elite counterparts and in this sense, the Matthew effect is not just cumulative but asymmetric. By this, we mean that the same strategy, in our case, volume growth, produces divergent outcomes depending on where in the hierarchy it is pursued (Zhang, 2025). The practical implications are equally telling. For journal editors, the findings of our paper challenge a common assumption that scaling output is a reliable route to upward mobility. While expansion may increase a journal’s visibility and provide more opportunities for citation, our analysis shows that such gains are marginal unless accompanied by quality improvements and broader reputational shifts. This is critically important as we have examined that volume growth only converts to increased prestige if there is a strong editorial and strategic background that selectively publish the best, or in other terms, “marketable” research. For editorial and publishing stakeholders, the results indicate that output expansion alone is weakly related to short-term changes in prestige indicators, and that baseline standing strongly conditions observed associations. This suggests that evaluations of ‘growth strategies’ should be calibrated to citation-lag dynamics and quartile inertia, and interpreted alongside non-bibliometric factors (e.g., selectivity, scope, editorial practice) that are not observable in SCImago. We understand that this is crucially difficult, especially in the current, highly diluted and overwhelmed editorial process. Nonetheless, prestige-improving strategies must follow a complex and challenging path in order to achieve higher impact – the growth in volume is just an important step but by far not the only one to take, in this context. For publishers, the message is even more strategic. Scaling portfolios indiscriminately may generate short-term revenue through article processing charges, but it risks undermining prestige, or in more Bourdieusian terms, scholarly capital (cf. Demeter, 2020 ) if journals become bloated without corresponding citation impact. Consequently, sustainable prestige strategies, particularly in the crowded landscape of publishing, require a careful balance between volume, visibility, and quality control. Lastly, for policymakers and research evaluators, the findings raise concerns about the heavy reliance on quartile rankings as shorthand measures of quality. This is not a novel critique as mentioned earlier but one that needs for accentuating and investigation. Quartile hierarchies are highly inertial, seemingly resistant to change, and disproportionately reward those already at the top. Thus, by embedding these hierarchies in evaluation systems, policymakers risk reinforcing inequalities between journals, disciplines, and regions, entrenching a publishing ecosystem where mobility is structurally constrained. These results can also be situated within the broader literature. Derek de Solla Price’s seminal insight into the exponential growth of science continues to hold, but with a crucial caveat where growth does not translate evenly into influence. We find this a pivotal takeaway since the volume of publications may continue to climb incessantly but prestige accrues selectively, clustered around a few dominant nodes in network terms. Demeter’s ( 2020 ) work on global inequalities in publishing resonates here: the expansion of journals in the Global South or in less prestigious disciplines does not automatically translate into upward movement in prestige metrics, largely because the system is oriented around entrenched Western and disciplinary centres. Furthermore, Seglen’s ( 1997 ) critique of journal impact factors also finds empirical support in this study. The SJR quartile system, while more sophisticated than raw impact factors, still exhibits the same slow-moving, path-dependent inertia that makes it a poor indicator of dynamic quality shifts. What we wished to add to these concerns with this study is but a nuance; while volume growth does have measurable, positive associations with prestige, these effects are small, unevenly distributed, and conditioned by historical standing. In other words, growth matters, but it matters differently depending on where a journal starts. The evidence also complicates some optimistic claims in recent literature about the democratizing potential of digitalization and open access (see Frank et al. (2023) who underlines the key flaws of the open access publication trend). It is unarguable that the digitization has, indeed, enabled many journals to increase their volumes by removing the financially challenging constraints of print-publishing, the benefits of this expansion remain heavily skewed. Q1 journals can scale almost without penalty, while Q4 journals risk being perceived as peripheral regardless of their output. The persistence of quartile hierarchies despite the removal of material constraints suggests, therefore, that prestige is less about technological capacity and more about entrenched symbolic capital (Putnam, 2009 ). This reaffirms Bourdieu’s ( 1984 ; 1988 ) argument that academic publishing operates as a field structured by symbolic power, where prestige is accumulated and defended like any other form of capital (also see Demeter, 2020 ; Roumbanis, 2019; Pellandini-Simányi, 2014 ). One of the most critical reflections arising from the study concerns the temporal lag in prestige accumulation. Evidently, citations accrue slowly, SJR score updates gradually, and quartile positions shift only incrementally. However, these progresses make prestige a “lagging indicator,” one that is resistant to short-term interventions from a holistic perspective. For editors and publishers, this means that even well-executed strategies may take years, if not decades, to bear fruit in the metrics that matter for reputation and for scholars and evaluators, it means that prestige signals often reflect past performance more than present quality. The aforemenioned lag also creates a paradox that is to be considered by all actors in academic knowledge production: the publishing system demands constant innovation and adaptation, yet the rewards are distributed according to historical legacies. In more metaphorical terms, this paradox is akin to running a marathon where the starting lines are historically fixed and where newcomers must keep innovating just to move forward, yet the distance to the leaders is measured not by present speed but by how far ahead they began. On a final constructive-critical note, we propose that the findings invite reflection on the normative desirability of such a system. If we were to accept that prestige is path-dependent, cumulative, and exceedingly resistant to change, then the reliance on prestige metrics in research evaluation may not simply reflect quality but actively reproduce inequality. This is supported by the fact that journals at the top continue to accumulate symbolic capital regardless of incremental shifts in quality, while those at the bottom remain “stigmatized” despite efforts to expand and improve. From this, a form of epistemic injustice emerges where knowledge from peripheral venues is systematically devalued not simply because of its intrinsic merit but because of the structural inertia of the prestige system. We understand that future policy implications are to consider that if quartile rankings and SJR scores continue to serve as dominant proxies for quality, they will reinforce stratification rather than level the playing field, therefore, alternatives that recognize the diversity of scholarly contributions, whether through qualitative assessments, field-sensitive metrics, or broader conceptions of impact, are urgently needed. In conclusion, the central takeaway of this study is that prestige in academic publishing cannot be engineered through volume alone. Answering the premise, therefore, would look something akin to this: greater volume may bring prestige, but only when layered on top of an already favorable historical trajectory. Thus, greater output may support growth in influence for some journals, but it is no guarantee of upward mobility in a system governed by historical inertia. The Matthew effect ensures that those at the top continue to pull away, while those at the bottom struggle to climb. Path dependence cements these patterns, making prestige less a function of current performance and more an echo of past trajectories. This study is subject to several limitations that temper the interpretation of its findings. First, although the dataset is extensive, it relies exclusively on SCImago (derived from Scopus) and aggregates 24 heterogeneous subject categories under the umbrella of “Social Sciences.” This approach provides a valuable “big picture,” but it inevitably obscures disciplinary specificities, such as, for instance, psychology’s faster citation cycles compared to law’s slower-moving traditions. Second, the reliance on SCImago’s SJR and quartile classifications introduces measurement constraints. Quartiles are relative positions within category-year distributions and shift with changes in the underlying journal pool, while SJR score is a slow-moving, network-weighted indicator. Moreover, the use of a three-year citation window privileges recent surges and disadvantages journals in fields where impact accrues more gradually. In this regard, a further limitation arises from the use of the “Best Q” rule, which assigns journals the highest quartile they achieve across multiple subject categories. While simplifying analysis, this may artificially inflate the standing of multidisciplinary journals and confound the interpretation of quartile mobility, which might in part reflect reclassification rather than genuine changes in prestige. Methodologically, the use of a gap-tolerant lag–lead structure to connect years with missing data maintains longitudinal coverage but risks misrepresenting short-term fluctuations when journals reappear after lapses in indexing or inactivity. The modelling strategy also remains associational rather than causal: regression and Markov analyses identify statistical patterns but cannot disentangle whether volume growth drives prestige, or whether already-prestigious journals expand because they can. Important confounding factors, such as editorial selectivity, topical novelty, acceptance rates, language of publication, open access models, and publisher policies, were not included but may critically shape outcomes. Finally, the dataset was harvested in August 2025, but the analytic window ended in 2024; because SCImago periodically updates its backfiles, later replications may yield slightly different slopes, distributions, or transition probabilities. Taken together, these limitations do not undermine the broad conclusions about path dependence and stratification, but they do suggest that the results should be interpreted with diligence and supplemented by future work at finer disciplinary scales, with more diverse indicators, and using designs better suited to distinguishing compositional change, lag structure, and within-journal dynamics. Declarations Author Contribution GFL and ZsK wrote the main manuscript text. GFL prepared the analysis and the figures. 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Technol. 40 , 359–365 (2020). https://doi.org/10.14429/djlit.40.01.14866 Oltheten, E., Theoharakis, V., Travlos, N.G.: Faculty Perceptions and Readership Patterns of Finance Journals: A Global View. J. Financial Quant. Anal. 40 , 223–239 (2005). https://doi.org/10.1017/s0022109000001800 Pellandini-Simányi, L.: Bourdieu, Ethics and Symbolic Power. Sociol. Rev. 62 , 651–674 (2014). https://doi.org/10.1111/1467-954x.12210 Puehringer, S., Rath, J., Griesebner, T.: The political economy of academic publishing: On the commodification of a public good. PLoS ONE. 16 , e0253226 (2021). https://doi.org/10.1371/journal.pone.0253226 Putnam, L.L.: Symbolic capital and academic fields. Manage. Communication Q. 23 , 127–134 (2009). https://doi.org/10.1177/0893318909335420 Roumbanis, L.: Symbolic Violence in Academic Life: A Study on How Junior Scholars are Educated in the Art of Getting Funded. Minerva. 57 , 197–218 (2018). https://doi.org/10.1007/s11024-018-9364-2 Sasvári, P., Lendvai, G.F.: On the Periphery of the European Social Sciences—A Scientometric Analysis of Publication Performance, Excellence, and Internal Bias in Social Sciences in the Visegrad Countries. Social Sci. 13 , 537 (2024). https://doi.org/10.3390/socsci13100537 Savage, W.E., Olejniczak, A.J.: More journal articles and fewer books: Publication practices in the social sciences in the 2010’s. PLoS ONE. 17 , e0263410 (2022). https://doi.org/10.1371/journal.pone.0263410 Seglen, P.O.: Why the impact factor of journals should not be used for evaluating research. BMJ. 314 , 497 (1997). https://doi.org/10.1136/bmj.314.7079.497 Singh, G.G.: Prestige risks homogenizing and hampering academia. Nature. 610 , 630 (2022). https://doi.org/10.1038/d41586-022-03406-z Thelwall, M., Sud, P.: Scopus 1900–2020: Growth in articles, abstracts, countries, fields, and journals. Quant. Sci. Stud. 3 , 37–50 (2022). https://doi.org/10.1162/qss_a_00177 Traag, V.A.: Inferring the causal effect of journals on citations. Quant. Sci. Stud. 2 , 496–504 (2021). https://doi.org/10.1162/qss_a_00128 Trueblood, J.S., Allison, D.B., Field, S.M., et al.: The misalignment of incentives in academic publishing and implications for journal reform. Proceedings of the National Academy of Sciences 122:. (2025). https://doi.org/10.1073/pnas.2401231121 Ujum, E.A., Kumar, S., Ratnavelu, K., Prathap, G.: A new journal power-weakness ratio to measure journal impact. Scientometrics. 126 , 9051–9068 (2021). https://doi.org/10.1007/s11192-021-04132-5 Vȋiu, G.-A., Păunescu, M.: The lack of meaningful boundary differences between journal impact factor quartiles undermines their independent use in research evaluation. Scientometrics. 126 , 1495–1525 (2021). https://doi.org/10.1007/s11192-020-03801-1 Wakefield, R.: Networks of accounting research: A citation-based structural and network analysis. Br. Acc. Rev. 40 , 228–244 (2008). https://doi.org/10.1016/j.bar.2008.03.001 Walters, W.H.: Information sources and indicators for the assessment of journal reputation and impact. Ref. Librarian. 57 , 13–22 (2016). https://doi.org/10.1080/02763877.2015.1088426 West, J.D., Bergstrom, T.C., Bergstrom, C.T.: The Eigenfactor MetricsTM: A network Approach to assessing Scholarly journals. Coll. Res. Libr. 71 , 236–244 (2010). https://doi.org/10.5860/0710236 Xie, Y., Wu, Q., Li, X.: Editorial team scholarly index (ETSI): an alternative indicator for evaluating academic journal reputation. Scientometrics. 120 , 1333–1349 (2019). https://doi.org/10.1007/s11192-019-03177-x Zhang, C., Liu, X., Xu, Y., Wang, Y.: Quality-structure index: A new metric to measure scientific journal influence. J. Am. Soc. Inform. Sci. Technol. 62 , 643–653 (2011). https://doi.org/10.1002/asi.21487 Zhang, T.: Will the increase in publication volumes dilute prestigious journals’ impact factors? A trend analysis of the FT50 journals. Scientometrics. 126 , 863–869 (2020). https://doi.org/10.1007/s11192-020-03736-7 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8745328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589498402,"identity":"1d44d696-a9fc-4095-bc4c-304285b30e3d","order_by":0,"name":"Gergely Ferenc 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(SCImago)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/769d4c4791d72087e344621e.png"},{"id":102501255,"identity":"7e9298a3-a298-4d55-8577-63146dcaea57","added_by":"auto","created_at":"2026-02-12 10:29:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":226002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuartile-level trends\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/7e12f7c7b8b29ad1d128e3b2.png"},{"id":102501256,"identity":"3462e2b4-9b19-4c5a-8598-d8939e7eb1de","added_by":"auto","created_at":"2026-02-12 10:29:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation analyses\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/d6198c6de88031f65bb21d71.png"},{"id":102501257,"identity":"7b5acb85-9ce7-4a84-8af7-b9e76b7dc3d5","added_by":"auto","created_at":"2026-02-12 10:29:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds ratios for quartile prediction with taking into account previous SJR scores (in absolute values), previous citation metrics, and the change % change in volume\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/2c919655364eb91e40ed2888.png"},{"id":102501252,"identity":"3596c7bb-0e89-4558-be75-aad91a82ee6b","added_by":"auto","created_at":"2026-02-12 10:29:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWinsorised Quartile transition analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/cdd117e4b0de35036b1d258e.png"},{"id":104779031,"identity":"3c9938ea-07c2-43a7-9cc6-8e0fb88f90de","added_by":"auto","created_at":"2026-03-17 07:29:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1681761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/8aab6d97-82a8-4813-a27c-303a13b3fe1a.pdf"},{"id":102746537,"identity":"ec552208-c8d9-4ae6-831e-417ab196805a","added_by":"auto","created_at":"2026-02-16 08:58:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":119279,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8745328/v1/31250bdf654d33b1546c7cec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn \u003cem\u003eLittle Science, Big Science\u003c/em\u003e, Derek J. de Solla Price (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) \u0026ndash; one of the most influential figures in scientometrics and science measurement (Danesh \u0026amp; Mardani-Nejad, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garfield, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dusek, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u0026ndash; offered an observation that did not originate in a statistical laboratory, but rather in the quiet company of bound volumes of old scientific journals. Trained as a physicist and later turning his attention to the history of science, as anecdotally presented by Wang and Barabasi (2021), Price kept the issues of \u003cem\u003ePhilosophical Transactions\u003c/em\u003e by his bedside table, reading every issue meticulously. Price sorted each volume by year and began to notice a revelational occurrence \u0026ndash; each year the piles of issues grew. He then began by counting; page by page, volume by volume the number of volumes and papers published over the 28 piles that had seemed to form an exponential growth curve (Wang \u0026amp; Barabasi, 2021). What he noticed was startling in its regularity \u0026ndash; the total number of titles seemed to grow with uncanny consistency and when he plotted these counts on semi-logarithmic paper, the points aligned into a straight line: the unmistakable signature of exponential growth. To Price, this was not merely a numerical curiosity. Much rather, it suggested that science behaved like an organism to stick with Wang and Barabasi\u0026rsquo;s (2021) bacteria example regarding the sustainability of exponential growth whereas the science \u0026ldquo;produced\u0026rdquo; was expanding in bursts and sustaining itself through an ever-growing network of communication. This realisation, grounded in the simple act of tallying old pages, became one of the earliest empirical portraits of science as a self-reinforcing social system, and it remains a touchstone in bibliometric thought. Over the past six decades, it can be generally stated that academic knowledge production has undergone radical changes that Price could scarcely have imagined. The listing of all perimeters pertaining to this change could fill up dozens of monographs, however, exemplary or \u0026ldquo;disruptive\u0026rdquo; events can be traced back to the digitization of scholarly content, the rise of online submission systems, the proliferation of open-access and mega-journals, the rise of predatory outlets and unethical publishing practices that prioritize quantity over quality by all means, and the globalisation of scholarly authorship have reshaped the production and circulation of academic work. These developments have also removed many material barriers to increasing output, allowing journals to scale their publication volumes far beyond the constraints of print-era page budgets and even \u0026ldquo;opened up\u0026rdquo; science to traditionally marginalised regions \u0026ndash; though it remains largely contested how successful this scope-widening was (see Demeter, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Yet, despite this apparent abundance, academic prestige remains unevenly distributed and remarkably resistant to rapid change.\u003c/p\u003e \u003cp\u003eThis polemic can be substantiated by a plethora of factors. Historically, it can be said that prestige in scholarly publishing is both symbolically and materially consequential (Merton, 1968; Bourdieu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; 1986; Burris, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; De Cruz, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Singh, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demeter, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kohus et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lendvai et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Morales et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In practice, this could be translated to a rather simple assumption where high-prestige journals attract more and higher-quality submissions, secure greater visibility in citation databases, and influence research agendas within and beyond their disciplines (see Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kwiek, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Demeter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Langin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Naturally, the field of scientometrics have moved way beyond this general observation. For instance, in the contemporary bibliometric ecosystem, prestige is often operationalised through complex quantitative composite indicators such as the quartile assigning which weights citations by the influence of the citing sources and classifies journals into quartiles (Q1\u0026ndash;Q4) within their subject categories (Ma\u0026ntilde;ana-Rodr\u0026iacute;guez, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Though quartile assignments differ in different repositories, mostly because they use different metrics to assign them (see Okagbue et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), in general, quartile rankings function as shorthand hierarchies (see Kosyakov \u0026amp; Pislyakov, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mason \u0026amp; Singh, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Black et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While the use of quartiles to rank journals are heavily criticized, Q1 journals are seen as leaders and gatekeepers, Q4 journals as marginal or peripheral (Seglen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Vȋiu \u0026amp; Păunescu, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garc\u0026iacute;a et al., 2021; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; K\u0026ouml;hler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although quartiles are relative measures that shift with changes in the broader distribution, the movement of individual journals between quartiles tends to be slow and uneven, reflecting underlying structural inertia (Garfield, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vȋiu \u0026amp; Păunescu, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Seglen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Kosyakov \u0026amp; Pislyakov, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present paper, two theoretical perspectives are used to illustrate and explain this inertia. The first is cumulative advantage, commonly known as the \u0026ldquo;Matthew effect\u0026rdquo; (Merton, 1968), which predicts that journals already occupying prestigious positions are better able to attract submissions, generate citations, and maintain or increase their impact. The second is path dependence, which holds that initial conditions \u0026ndash; such as historical citation patterns or long-standing editorial reputations \u0026ndash; exert a continuing influence, making large positional shifts rare. Such path dependence can be seen in preferential attachment and citation concentration (Kozlowski et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), editorial bias and influence (Xie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kulal et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) among many other aspects of publishing. All in all, these perspectives suggest that the quartile hierarchy may be self-reinforcing, with mobility constrained for both top and bottom tiers.\u003c/p\u003e \u003cp\u003eInterconnecting Price\u0026rsquo;s scholarship and the above brief lamentation on prestige, against the above backdrop, one might rightly ask: does increasing a journal\u0026rsquo;s publication volume offer a viable path to higher prestige? The intuitive answer is mixed, even more so, since the complexity of academic publishing and its economic aspects play a crucial part in assessing the above query (Puehringer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kwiek, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trueblood et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Seglen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Brembs, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Goel \u0026amp; Faria, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). On the one hand, publishing more articles increases the surface area for potential citations, raises journal visibility, and may signal vitality or growth to authors and readers (Drivas \u0026amp; Kremmydas, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, expansion is \u0026ldquo;risky\u0026rdquo; (Zhang, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and can dilute selectivity, potentially reducing average article quality and citation potential. Moreover, the mechanics of citation accumulation (or the SJR score calculation as presented later in this paper) mean that benefits from increased volume are unlikely to appear immediately; prestige metrics are often \u0026ldquo;lagging indicators,\u0026rdquo; sensitive to sustained patterns rather than short-term spikes. Despite the strategic and theoretical relevance of this question, existing evidence is scarce and fragmented. Some studies have documented broad increases in publication volumes across fields, regions, and publishers (Kim, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thelwall \u0026amp; Sud, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Csom\u0026oacute;s \u0026amp; Farkas, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but few have linked these changes directly to subsequent shifts in prestige. Analyses that do address this link often focus on either short-term correlations or long-term averages, without integrating both perspectives and even fewer studies consider how the effect of volume growth might differ across the quartile spectrum, potentially privileging Q1 journals with strong brand effects while offering little to Q4 journals whose outputs circulate on the periphery of citation networks. Problematizing volume in the context of impact is also critical in understanding the ever-growing number of scholarly literatures. Each year, the number of publications grow excessively (Thelwall, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bornmann et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), however, the impact of this volume growth or the strategic patterns in how impact correlates with growth is still a developing field of scientometrics.\u003c/p\u003e \u003cp\u003eIn the present endeavor, the focus lies on one particular field, the social sciences (SS). The SS provide a particularly compelling arena for exploring these issues since they comprise a heterogeneous mix of disciplines from economics and psychology to sociology and education each with distinct authorship patterns, topical dynamics, and norms around publishing frequency. Unlike many STEM fields, where rapid methodological innovation can drive swift changes in journal impact, social science fields often exhibit slower-moving citation patterns and longer time horizons for prestige accumulation as well as vastly different venue preferences (see Savage \u0026amp; Olejniczak, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This makes it possible to observe structural tendencies \u0026ndash; such as stratification or stability \u0026ndash; over extended periods without the distortions caused by sudden, field-wide paradigm shifts. The present study builds on Price\u0026rsquo;s foundational insight by examining how growth in journal output interacts with the stratified prestige structure of the social sciences. It approaches the question from both a structural and a dynamic perspective: structurally, by mapping the evolution of volumes, citations, and SCImago Journal Ranking scores (hereinafter referred to as: SJR score) across quartiles over two decades; dynamically, by assessing whether changes in volume predict short-term or long-term shifts in prestige, and whether these effects are uniform across the quartile hierarchy. Furthermore, it also seeks to illuminate not only the mechanics of prestige accumulation but also the extent to which expansion strategies can disrupt \u0026ndash; or reinforce \u0026ndash; the existing hierarchy. Rather than treating short-term analyses as a test of \u0026ldquo;instant payoff,\u0026rdquo; the study conceptualises them as a test of prestige inertia under metric and citation-lag constraints. Lastly, we underline that in the present paper, we do not treat short-term analyses as a test of \u0026ldquo;instant payoff.\u0026rdquo; On the contratry, our study conceptualises them as a test of prestige inertia under metric and citation-lag constraints.\u003c/p\u003e \u003cp\u003eThree research questions (RQ) guide the analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eHow have publication volumes, citation counts, and SJR scores evolved across quartiles (Q1\u0026ndash;Q4) in social sciences journals between 2004 and 2024, and what do these trajectories reveal about stratification in journal prestige?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe primary purpose of RQ1 is to establish the empirical baseline of the study by mapping long-term trends in output, citations, and prestige across quartiles. Rather than merely documenting overall growth, this question examines how expansion and impact have been distributed within the prestige hierarchy. RQ1 assesses whether growth has been broadly shared or disproportionately concentrated among higher-ranked journals, thereby providing the structural context for the subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eTo what extent do annual changes in publication volume coincide with changes in citations, SJR scores, and quartile positions, given the lagged nature of citation accumulation and the construction of prestige metrics?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eRQ2 shifts the analytical focus from long-term trajectories to short-term dynamics. Instead of treating annual volume expansion as a strategy expected to yield immediate prestige gains, this question examines whether prestige indicators exhibit responsiveness or inertia in the short run. Subsequently, RQ2 complements RQ1 by testing whether the stratified structure identified at the macro level is reflected in limited short-term mobility, or whether journals experience measurable contemporaneous shifts in prestige following changes in output.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eHow strongly does historical journal standing (measured by prior SJR scores and citation levels) condition subsequent prestige outcomes relative to contemporaneous changes in publication volume, and do these relationships differ across quartiles?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eRQ3 integrates the structural patterns identified in RQ1 with the short-term dynamics examined in RQ2 by explicitly foregrounding the role of accumulated advantage. It investigates whether historical prestige dominates subsequent outcomes and conditions the association between volume growth and changes in prestige indicators. The goal of RQ3 is to clarify whether expansion operates differently depending on a journal\u0026rsquo;s initial position in the hierarchy and helps explain why long-term divergence may persist despite weak short-term responsiveness.\u003c/p\u003e \u003cp\u003eVia situating these questions within the dual frameworks of cumulative advantage and path dependence, the study addresses a central tension in scholarly publishing: whether expansion in scale can serve as a mechanism for upward mobility in a prestige hierarchy that appears resistant to change. The findings have implications for editors deciding whether to scale their journals, for publishers balancing commercial and reputational goals, and for policy-makers whose reliance on quartile rankings influences the incentives that shape scholarly behaviour. More broadly, they contribute to ongoing debates about the nature of growth in science, suggesting that while the volume of published work continues to rise, the benefits of this expansion may accrue disproportionately to those already at the top.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eThe dataset for this study was drawn from the publicly available SCImago Journal \u0026amp; Country Rank (or SJR) portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scimagojr.com/\u003c/span\u003e\u003cspan address=\"https://www.scimagojr.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, one of the most renowned and utilized journal repositories which compiles bibliometric indicators derived from Elsevier\u0026rsquo;s Scopus database (Gonz\u0026aacute;lez-Pereira et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jacs\u0026oacute;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). SCImago provides a comprehensive set of annual metrics for indexed journals, including total documents published, total citations received, the SJR score, and quartile classification within subject categories (Gonz\u0026aacute;lez-Pereira et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Falagas et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jacs\u0026oacute;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The journal selection was conducted as follows. First, only journals assigned to the Social Sciences subject area were included, all other publication venues (book chapters, conference proceedings) were excluded. This subject area is one of the largest in the SCImago system with 24 distinct subject categories ranging from anthropology to law to urban studies (Sasv\u0026aacute;ri \u0026amp; Lendvai, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although the overall period spans 21 years, with 2024 used as a reference for 2025, the primary analysis focuses on a 20-year period from 2005 to 2024. For the forecast for 2005, the year 2004 was also included as a necessary reference point. Data was collected on 1 August 2025.\u003c/p\u003e \u003cp\u003eA critical limitation shall be mentioned in terms of data employed. SCImago, though builds on Scopus\u0026rsquo;s system, does use a slightly different categorization system. Therefore, the 24 subject categories are handled under one \u0026ldquo;branch\u0026rdquo; in the framework where discipline-specific differences are to be evaluated journal-by-journal or by subject categories. Since we handled all subject categories together, we did not differentiate between different disciplines or fields when evaluating the results. Nonetheless, we do encourage future research endeavors to explore the disciplinary differences on a micro-scale as well, since our study solely provides the \u0026ldquo;big picture\u0026rdquo; on social sciences.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data extraction and preparation\u003c/h2\u003e \u003cp\u003eThe full list of journals in the Social Sciences subject area was downloaded from SCImago for each year between 2004 and 2024. For each journal-year observation, all variables were extracted, however, we filtered indicators selectively in accordance with our research scope: The indicators included were the following:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eYear\u003c/b\u003e \u0026ndash; the calendar year of the metric.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuartile\u003c/b\u003e \u0026ndash; Q1, Q2, Q3, or Q4 classification within the journal\u0026rsquo;s primary SCImago subject category. In some cases, SCImago includes journals without a quartile assignment (these are indicated with a \u003cem\u003e\u0026ldquo;-\u0026ldquo;\u003c/em\u003e in the dataset). We only included these journals where quartile examination was not present. In the dataset, for each journal in each year a Best Q is assigned. This means that in the particular year the respective journal is assigned a Q or maybe Qs in multiple subject categories, however, since we worked with the \u0026ldquo;umbrella\u0026rdquo; subject area, Best Q signifies that a journal has achieved its best Q ranking in terms of all Qs assigned in the subject categories it is categorized in. In more practical terms, if XY journal is assigned four different Qs in four subject categories under the Social Sciences subject area (X \u0026ndash; Q1, Y \u0026ndash; Q2, Z, - Q3, A \u0026ndash; Q2), the Best Q will be Q1 since it is the best Q it has achieved. This, as mentioned above, only means that we analyzed the holistic landscape and did not take into account the differences in specific subject categories (the statistics of journals in SCImago with missing Qs can be found under Appendix 2).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTotal documents\u003c/b\u003e \u0026ndash; the total number of citable documents (articles, reviews, conference papers) published in that year.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTotal citations\u003c/b\u003e \u0026ndash; the total citations received by the journal in that year. For this, Total_Cites (3y) was used which signifies the total citations received in the last 3 years of the respective index-year. This also happens to be the main citation metric used by SCImago Journal Rank.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSJR score\u003c/b\u003e \u0026ndash; the SCImago Journal Rank value for that year (Gonz\u0026aacute;lez-Pereira et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The SJR score (sometimes referred to as \u0026ldquo;indicator\u0026rdquo;) is a sophisticated metric that is defined by Gonz\u0026aacute;lez-Pereira et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) as a \u0026ldquo;size-independent metric aimed at measuring the current \u0026ldquo;average prestige per paper\u0026rdquo; of journals for use in research evaluation processes.\u0026rdquo; The SJR score is based on multiple metrics such as citation weighting schemes, citation network (including centrality of the citations), in order to indicate where the journal in the respective discipline was in terms of relevancy and impact (Sasvari \u0026amp; Lendvai, 2024; Gonz\u0026aacute;lez-Pereira et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Though the uniformization of impact assessment of SJR scores would not be adequate, per Sasvari and Lendvai (2024), a general rule of thumb is that if an SJR score is higher than or equivalent to 1.00, it might be correct to assume that the respective journal is impactful (or elite, in more qualitative terms), while journals with an SJR score of less than 1.00 might be considered less impactful in terms of purely quantitative terms.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eJournal identifier\u003c/b\u003e \u0026ndash; a unique string to track journals over time. We used this to effectively identify individual journals for our analyses.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAll years in which a journal was absent from SCImago\u0026rsquo;s Social Sciences listings (due to indexing gaps, category reassignments, or inactivity) were recorded as missing and treated according to the procedures described below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Variable transformations\u003c/h2\u003e \u003cp\u003eThree types of measures were derived from the raw indicators:\u003c/p\u003e \u003cp\u003eFor descriptive trend analysis, annual mean and median values of total documents, total citations, and SJR score were calculated for each quartile (mean and median values per quartile-year). This enabled the comparison of central tendency measures and the detection of skewness (e.g., whether mean values were inflated by a small number of extreme journals). Since we were interested in percentage changes (%Δ), a Year-to-year percentage change was computed for each journal\u0026rsquo;s total documents, total citations, and SJR score. For example, the percentage change in total documents for journal \u003cem\u003ei\u003c/em\u003e between years \u003cem\u003et\u003c/em\u003e and \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\%\\:\\varDelta\\:\\:\\text{docs}\\:=\\:\\frac{{\\text{docs}}_{t+1}\\:-\\:{\\text{docs}}_{t}}{{\\text{docs}}_{t}}\\:\\times\\:\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis allowed the analysis of short-term fluctuations independent of absolute size differences. Lastly, annual differences (Δ) were also computed, especially, for some models (e.g., change in SJR score) as absolute differences rather than percentage changes were used to capture directional movement in prestige metrics.\u003c/p\u003e \u003cp\u003eTo preserve longitudinal continuity in the analysis, a gap-tolerant lag\u0026ndash;lead structure was used. This means that where a journal was missing in one or more intermediate years but reappeared later, its next available year was paired with its most recent prior year to compute changes. This avoided the common bias of dropping journals with incomplete annual coverage and allowed the inclusion of longer trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analytical strategy\u003c/h2\u003e \u003cp\u003eThe study combined descriptive statistics, association tests, regression modelling, Markov chain analysis, and cluster analysis to examine the relationships between publication volume and prestige from multiple angles. Each method addressed a different dimension of the research questions. For each quartile (Q1\u0026ndash;Q4), annual means and medians of total documents, total citations, and SJR scores were plotted over the 2004\u0026ndash;2024 period. Comparing mean and median curves allowed identification of skewness, for example, whether quartile-level growth was driven by a subset of high-volume, high-impact journals. Linear trend slopes were calculated for each indicator by quartile, and compound annual growth rates were computed to quantify percentage growth over the full period. Pearson correlations were computed between %Δ documents and %Δ citations, and between %Δ documents and Δ SJR score, to assess linear relationships between short-term output changes and prestige changes. Spearman rank correlations were calculated between %Δ documents and quartile improvement (binary indicator of whether the journal moved to a higher quartile in the following year) to capture monotonic but potentially non-linear associations.\u003c/p\u003e \u003cp\u003eWe applied three regression models, too to sophisticate our specialized examinations. We employed the following models:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOrdinal logistic regression (VGAM package in R)\u003c/b\u003e was used to predict quartile rank (Q1\u0026ndash;Q4) in year t\u0026thinsp;+\u0026thinsp;1 from %Δ documents, prior SJR scores, and prior citations in year t. Odds ratios were reported to indicate the relative effect sizes (RQ1 and RQ2).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLinear regression models\u003c/b\u003e predicted Δ SJR scores and %Δ citations in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e from %Δ documents, prior quartile, prior SJR, and prior citations. Interaction terms between %Δ documents and prior quartile were included to test whether the effect of expansion differed by starting position (see RQ2).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGeneralised additive models (GAMs)\u003c/b\u003e were estimated to explore non-linear relationships between %Δ documents and prestige changes, with smooth terms fitted for prior SJR scores and prior citations (RQ3).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eLastly, for RQ3, a Markov chain, a \u003cem\u003e4\u0026times;4\u003c/em\u003e transition probability matrix was constructed for quartile mobility, showing the likelihood of moving from quartile \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e to quartile \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e. This was computed for the full dataset, for journals above the median %Δ documents (high-growth group), and for those below the median (low-growth group). Probabilities were compared across groups to assess whether higher volume growth was associated with greater upward mobility.\u003c/p\u003e \u003cp\u003eTo add rigor to the above analyses, three robustness checks were conducted to ensure stability of the results using a Winsorised model based on extreme %Δ document values at the 1st and 99th percentiles to limit outlier influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Cluster analysis\u003c/h2\u003e \u003cp\u003eLastly, for the cluster analysis with interconnection to RQ3, a hierarchical clustering (Ward.D2 linkage) was applied to standardised longitudinal indicators, including median and interquartile range of %Δ documents and %Δ citations, median and IQR of Δ SJR, quartile improvement probability, document and SJR score slopes, and Kendall correlation between documents and SJR. The optimal number of clusters (\u003cem\u003ek\u003c/em\u003e) was determined by silhouette width analysis, and principal components analysis (PCA) was used for visualisation. Clusters were profiled in terms of stability, volatility, and output\u0026ndash;prestige coupling. The results are explotary in nature and solely serve the function to invite future scholarship to reflect in clustering mechanisms when studying prestige and scholarly output connections. We report the results in Appendix 3.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General results on the change of volume and \u0026ldquo;prestige metrics\u0026rdquo; (RQ1)\u003c/h2\u003e \u003cp\u003eBetween 2004 and 2024, the analysis of the examined journals shows clear upward trends in journal output, citation counts, and SJR scores, with notable differences between mean and median values. Mean citation counts rose sharply from around 54 in 2004 to over 350 in 2024 which is a substantial increase in aggregate citation volumes. However, median citations increased more modestly from 15 to 61 reflecting that the influence is more accentuated in the case of a small number of highly cited journals. Mean journal volumes grew steadily from about 28 to over 50 papers per year, whereas median volumes remained relatively stable around 22\u0026ndash;27. Regarding SJR, the mean value increased from 0.38 to a peak above 0.50 in the mid-2010s before stabilising, while the median SJR score rose from 0.19 to roughly 0.26, again showing that a small subset of journals skews the average upward. The disparity between mean and median across all three indicators also confirms the presence of strong right-skewness in the dataset, driven by elite journals. Statistical summaries further support this statement. For citations, the standard deviation of the mean trend (82.31) is more than seven times that of the median trend (11.75) which can be converted to an extreme variability among top performers. The \u003cem\u003eR\u0026sup2;\u003c/em\u003e values show that time explains a large share of the variance for average journal volume (0.886) and median citations (0.836), but less for average SJR score (0.354), implying weaker temporal predictability in journal prestige. The SJR score median trend\u0026rsquo;s \u003cem\u003eR\u0026sup2;\u003c/em\u003e of 0.657 still suggests a moderate, consistent upward movement in typical journal standing, however, its statistical significance is not strong. Across the period, median journal volume remained strikingly constant, which may indicate structural stability in most journals\u0026rsquo; publishing capacity. In contrast, the rapid rise in mean citations from around 2016 onwards likely reflects the combined effects of larger publication volumes in certain outlets, shifts in citation practices, and perhaps increased internationalisation. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the full descriptive statistical results are available under Appendix 1)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTurning onto quartiles, for Q1, mean volume rose from 38.53 in 2004 to 91.62 in 2024, an absolute gain of 53.09 and a 137.79% increase, with a linear slope of 2.1748 papers per year. Q1 mean citations climbed from 157.88 to 1015.14, a rise of 857.26 or 542.98%, on the steepest citation slope of 35.8411 per year. Q1 mean SJR score increased from 0.920 to 1.068, up by 0.148 or 16.07%, with an annual slope of 0.005122. The number of Q1 journals expanded from 863 to 2396, adding 1533 titles for a 177.64% increase. For Q2, mean volume moved from 29.56 to 43.95, up 14.39 or 48.70%, with a slope of 0.6128 per year. Q2 mean citations rose from 39.52 to 174.42, a gain of 134.89 or 341.29%, on a 6.2503-per-year slope. Q2 mean SJR score advanced from 0.261 to 0.360, up 0.099 or 37.85%, with a 0.003430 annual slope. Q2 titles increased from 802 to 2167, adding 1365 journals for a 170.20% rise. For Q3, mean volume grew from 23.40 to 31.52, a gain of 8.12 or 34.70%, with a 0.3169 slope. Q3 citations went from 14.80 to 65.43, increasing by 50.63 or 342.11%, on a 2.4598 annual slope. Q3 SJR score edged up from 0.148 to 0.199, a 0.051 change or 34.63%, with a 0.002330 slope. Q3 journal counts expanded from 739 to 1941, adding 1202 titles for 162.65% growth. For Q4, mean volume slipped from 21.33 to 20.53, a decrease of 0.80 or \u0026minus;\u0026thinsp;3.76%, despite a nominal positive slope of 0.0761. Q4 mean citations improved from 4.18 to 20.22, up 16.04 or 383.49%, on a 0.8283-per-year slope. Q4 mean SJR score climbed from 0.105 to 0.124, a 0.018 increase or 17.15%, with the smallest slope at 0.000832. Q4 journal counts still expanded from 689 to 1813, adding 1124 titles for 163.13% growth.\u003c/p\u003e \u003cp\u003eThe volume ratio of Q1 to Q4 widened from 1.81 in 2004 to 4.46 in 2024 which points to a strong top-tier expansion in output relative to the bottom tier. The citation ratio of Q1 to Q4 stretched from 37.74 in 2004 to 50.19 in 2024 indicating an even faster divergence in impact. By contrast, the Q1\u0026ndash;Q4 SJR score ratio nudged slightly down from 8.72 to 8.64, suggesting small convergence in prestige despite persistent gaps. In 2024, Q2 citations were 2.67\u0026times; Q3\u0026rsquo;s. This finding shows a clear separation in (upper) middle-tier impact. Slope comparisons further underscore stratification: Q1\u0026rsquo;s citation slope is 5.73\u0026times; Q2\u0026rsquo;s and 43.27\u0026times; Q4\u0026rsquo;s, while its volume slope is 28.57\u0026times; Q4\u0026rsquo;s. On compound growth, Q1\u0026rsquo;s volume CAGR is 4.43% per year versus 2.00% for Q2, 1.50% for Q3, and \u0026minus;\u0026thinsp;0.19% for Q4. Citation CAGRs are high across the board \u0026minus;\u0026thinsp;9.75% (Q1), 7.71% (Q2), 7.72% (Q3), and 8.20% (Q4) \u0026ndash; but absolute levels remain far apart.\u003c/p\u003e \u003cp\u003eFor SJR, annual compound gains are 0.75% (Q1), 1.62% (Q2), 1.50% (Q3), and 0.79% (Q4), consistent with slow-moving prestige metrics. Journal counts compound at roughly 5.24% (Q1), 5.10% (Q2), 4.95% (Q3), and 4.96% (Q4) per year, showing broad expansion in the number of outlets. The combination of steep citation growth and rising volume in Q1 signals compounding advantages in both reach and recognition. Meanwhile, Q2 and Q3 improve steadily but not fast enough to close the gap with Q1. Overall, 2004\u0026ndash;2024 shows a publishing ecosystem where top-quartile journals compound both scale and impact, middle tiers make steady gains, and the lowest tier grows mainly in count but lags in output and influence.\u003c/p\u003e \u003cp\u003eIn more interpretative terms, the results point to an increasingly stratified scholarly publishing system in which Q1 journals consolidate dominance by expanding both their output and citation advantage at a much faster rate than all other quartiles. Although \u0026ldquo;mid-tier\u0026rdquo; journals, namely, Q2 and Q3 journals show \u0026ldquo;healthy\u0026rdquo; growth in citations and volume, their slower slopes mean they are unlikely to catch up with Q1 without structural changes in visibility, indexing, or funding. Q4 journals\u0026rsquo; stagnation in volume alongside modest citation gains suggests that expansion in their numbers does not translate into greater influence, potentially reinforcing a perception of low-tier irrelevance. On a more critical note, the widening citation and volume gaps point toward a reinforcing cycle where prestige, resources, and impact are disproportionately concentrated in the top quartile, making upward mobility increasingly difficult for lower-ranked journals. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Changes and trends in the dyad of prestige and volume (RQ2)\u003c/h2\u003e \u003cp\u003eSince we wanted to understand the above stratification in a more comprehensive manner, we employed several analyses that examine the relational aspects of changes in volume and impact. First, the association analyses reveal that short-term changes in publication volume show almost no statistical relationship with immediate shifts in prestige metrics. The Pearson correlation between %Δ volume and %Δ citations is only 0.024, essentially indicating no linear association. This means that, on average, years when journals increase their output are not systematically accompanied by proportional changes in citation counts in the same year. Similarly, the Pearson correlation between %Δ volume and Δ SJR scores is only 0.013, reinforcing that SJR scores do not respond strongly to annual fluctuations in output. For SJR scores we used the absolute values and not percentages in this case, since it better represented the \u0026ldquo;actual\u0026rdquo; change in a more interpretable way. The Spearman correlation between %Δ volume and quartile improvement is slightly higher at 0.055, but still negligible and presents that volume growth is not a consistent driver of moving into a better quartile rank. These extremely low coefficients remain despite large sample sizes (over 96,000 year-to-year journal observations) indicating the effect is practically non-existent.\u003c/p\u003e \u003cp\u003eThe weak correlations imply that volume changes alone are not a reliable tactical lever for boosting short-term prestige. This fits with theoretical expectations that citations accrue with a lag, and that SJR score is resistant to volatility due to its network weighting. The slight positive link to quartile improvement might reflect cases where sustained growth over multiple years eventually pushes journals into a higher quartile, but such effects are washed out in a purely annual correlation. Taken together, the results argue for caution in interpreting volume increases as a quick path to higher impact metrics. They also show the importance of multi-year perspectives in editorial strategy, as short-term boosts in publication counts rarely move prestige indicators in the same period. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Historical performance and predictive analyses (RQ3)\u003c/h2\u003e \u003cp\u003eWe then turned to predictive modelling to quantify how journal output growth relates to prestige shifts when looking at historical performance. We used the absolute values of SJR scores in this case as well.\u003c/p\u003e \u003cp\u003eThe ordinal logistic regression using VGAM predicted quartile rank (Q1\u0026ndash;Q4) from percentage change in publication volume, previous SJR, and previous citations. Results show that all predictors were highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A one standard deviation increase in %Δ volume increased the odds of being in a higher quartile by 8.61% (OR\u0026thinsp;=\u0026thinsp;1.086, 95% CI [1.073, 1.099]). Previous SJR score had the largest effect: a one standard deviation increases more than doubled the odds of a higher quartile (OR\u0026thinsp;=\u0026thinsp;2.194, 95% CI [2.142, 2.247]) and prior citations also mattered, with an OR of 1.119 (95% CI [1.081, 1.158]). Translating these into simpler that, these results essentially mean that journals with more historical citations were modestly more likely to improve in quartile and confirm that baseline prestige indicators dominate quartile changes, but volume growth still provides a measurable, positive contribution.\u003c/p\u003e \u003cp\u003eThe linear regression for Δ SJR scores found a small but highly significant effect of %Δ volume (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0205, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.42\u0026times;10⁻\u0026sup1;⁶). This means that, on average, a one standard deviation increase in volume change corresponds to a 0.0205-point increase in SJR considering other factors. Previous quartile had a small negative coefficient (\u003cem\u003eβ\u003c/em\u003e = -0.0027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055), suggesting a possible ceiling effect where higher-ranked journals gain less in SJR. Previous SJR scores had a strong negative relationship with Δ SJR scores (\u003cem\u003eβ\u003c/em\u003e = -0.0538, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), essentially reinforcing this ceiling effect where elite journals have less room to increase further. Prior citations had a small but significant positive association (\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eβ\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.0057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.06\u0026times;10⁻⁸). The interaction term between volume growth and prior quartile was positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0050, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00216), meaning that lower-quartile journals benefitted more in SJR score from increasing volume than higher-quartile ones. The linear regression for %Δ citations revealed that %Δ volume significantly predicted citation growth (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.286, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00037). This implies that a one standard deviation increase in volume change is associated with a 2.29 percentage-point increase in citation growth. Previous quartile was a strong positive predictor (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.226, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), meaning higher-quartile journals tend to experience much larger percentage citation increases year-to-year. Surprisingly, previous SJR score was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21) in this model, and prior citations had a marginally negative effect (\u003cem\u003eβ\u003c/em\u003e = -0.919, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). The interaction between volume growth and prior quartile was positive but only marginally significant (β\u0026thinsp;=\u0026thinsp;1.403, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055), hinting that expansion may slightly amplify citation gains more for top-tier journals.\u003c/p\u003e \u003cp\u003eOur third model was the GAM model for Δ SJR scores that showed that all three predictors (%Δ volume (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), previous SJR scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), and previous citations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶)) had significant non-linear effects. The smooth term for previous SJR score illustrated a steep decline in potential SJR score gains as baseline SJR score increased, confirming the ceiling effect from the linear model. The smooth term for %Δ volume showed that moderate increases in volume were associated with the largest consistent SJR score gains, while very high increases produced unstable or even negative returns. Prior citations displayed a hump-shaped pattern: benefits peaked at moderate historical citation counts before tapering off. The GAM model for %Δ citations also found a strong non-linear relationship for %Δ volume (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), where moderate expansion correlated with the largest proportional citation gains, but extreme increases could dampen citation growth. Previous quartile remained significant as a parametric term (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.817, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026times;10⁻\u0026sup1;⁶), reinforcing the structural advantage of high-ranked journals. Prior SJR scores had a weaker but still significant smooth effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), while prior citations were not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064) after taking into account other factors.\u003c/p\u003e \u003cp\u003eTaken together, the models show that previous SJR score is the most powerful driver of quartile position changes, dwarfing the effect of volume growth in magnitude. Citation history also matters, but its influence is smaller and more variable across models. Volume growth consistently predicts positive changes in prestige metrics, but its effect is modest and often strongest for journals starting from lower prestige positions. The interaction effects suggest an asymmetry. Lower-quartile journals gain more SJR score from expansion, while higher-quartile journals are better at turning expansion into citation growth. The GAM results, however, caution against assuming linear returns from growth; moderation often outperforms extreme expansion. The low adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e values across models (generally under 5%) highlight that much of the variation in prestige changes remains unexplained, suggesting that other factors like editorial selectivity, topical novelty, or author network effects play major roles.\u003c/p\u003e \u003cp\u003eAs mentioned in the Introduction, these results substantiate what we call a \u0026ldquo;path-dependent prestige system\u0026rdquo;. Let us expand briefly in the context of the results of this analysis. By path-dependent prestige system, we mean that top journals tend to remain at the top, benefitting disproportionately from their historical metrics confirming Merton\u0026rsquo;s (1968) Matthew effect. Lower-tier journals can climb the prestige ladder through controlled, quality-focused expansion, but face structural limits imposed by entrenched hierarchies. For citation growth, high-tier journals leverage both their brand and scale more effectively than their lower-tier counterparts. Strategically, the optimal approach for growth depends on the starting position: elite journals can safely scale, while emerging journals may need to balance volume increases with sustained quality improvements to break into higher quartiles. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLastly, we examined whether short-term changes in publication volume are associated with subsequent changes in journal prestige indicators (specifically SJR scores and quartile positions) treating these analyses as tests of responsiveness versus structural inertia rather than predictive effects.\u003c/p\u003e \u003cp\u003eThe first step was the construction of a gap-tolerant lagged panel dataset, which ensured that each journal-year observation was linked to its most recent prior available year, even if the years were not consecutive. This avoided the common pitfall of losing data due to missing intermediate years (e.g. a journal is indexed as a Q4 journal in 2022, falls out in 2023, then gets back in 2024 as a Q3 journal) and allowed for the inclusion of all available temporal relationships. For each observation, \u0026ldquo;lagged\u0026rdquo; predictors were calculated as follows: percent change in publication volume, previous year\u0026rsquo;s SJR, previous year\u0026rsquo;s total citations, and previous year\u0026rsquo;s quartile rank. Correspondingly, \u0026ldquo;lead\u0026rdquo; outcome variables were calculated for the following year: change in SJR, percent change in citations, and next-year quartile rank. This lag-lead structure created a dataset suitable for panel econometric modeling and Markov chain transition analysis.\u003c/p\u003e \u003cp\u003eFirst, two main panel regression models were estimated. The first predicted the change in SJR scores in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e as a function of percent change in volume in year \u003cem\u003et\u003c/em\u003e, checked with prior SJR score and prior citations. The second predicted the percentage change in citations in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e using the same predictors. Each model was run with both fixed-effects (FE) and random-effects (RE) specifications. The Hausman test was used to decide between FE and RE for each model, ensuring the treatment of unobserved heterogeneity was statistically sound. FE models controlled for time-invariant journal characteristics (e.g., disciplinary focus, indexing coverage), while RE models assumed that unobserved factors were uncorrelated with the predictors. In parallel, a Markov chain model was constructed to capture quartile mobility over time. For each journal-year observation, the quartile in the previously available year became the \u0026ldquo;from\u0026rdquo; state, and the quartile in the following year became the \u0026ldquo;to\u0026rdquo; state. This yielded a 4\u0026times;4 transition probability matrix describing the likelihood of moving between Q1, Q2, Q3, and Q4 from one year to the next. The analysis was conducted three times: for the full dataset (overall), for high-growth journals (above the median percent change in volume), and for low-growth journals (below the median). This allowed for direct comparison of mobility patterns by growth profile.\u003c/p\u003e \u003cp\u003eThe base case transition matrix revealed a high degree of persistence at the quartile extremes. Q1 journals had an 84.2% probability of remaining in Q1 year-to-year, with a 14.3% probability of dropping to Q2 and less than 2% chance of dropping further. Q4 journals had a 61.0% probability of staying in Q4, with a 30.7% chance of moving up to Q3, 7.7% to Q2, and almost no chance (0.6%) of leaping to Q1. The middle quartiles were more dynamic. Q2 journals moved up to Q1 in 21.6% of cases and down to Q3 in 15.1%, while Q3 journals moved up to Q2 in 27.2%, remained in Q3 in 59.6%, and dropped to Q4 in 10.3%. Direct non-adjacent jumps (e.g., Q3\u0026rarr;Q1) were rare, at only 2.9%. The panel regression results were notable for the extremely small coefficients on the volume growth variable. In the fixed-effects \u003cem\u003eΔSJR\u003c/em\u003e score model, the coefficient was 0.000089 which implies that even a very large increase in publication volume would be associated with a negligible change in SJR score in the following year. In practical terms, this means that increasing output alone without changes in citation quality or other impact-related factors is unlikely to move a journal into a higher quartile in the short term.\u003c/p\u003e \u003cp\u003eTo reiterate, strictly from a strategic perspective, this means that simply increasing publication volume (even substantially) is unlikely to produce rapid gains in quartile ranking. Other factors, such as improving citation impact, targeting higher-impact submissions, and enhancing journal visibility, are more likely to produce upward mobility. In sum, the integrated panel regression and Markov chain analysis \u0026ndash; combined with targeted robustness checks \u0026ndash; shows that short-term volume growth is not a significant driver of SJR score or quartile changes in the Social Sciences. The quartile mobility structure is highly stable, resistant to extreme cases, unaffected by the choice of growth metric, and robust to missing data. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Summary of the findings\u003c/h2\u003e \u003cp\u003eThe present study set out to examine the relationship between journal output growth and prestige in the social sciences over two decades, drawing on SCImago Journal Rank data from 2004\u0026ndash;2024. We combined descriptive statistics, correlation tests, regression modelling, Markov chain analysis, and cluster profiling, in order for the analysis to assess both long-term trajectories and short-term dynamics to clarify whether expansion in volume offers journals a pathway to higher prestige or whether historical hierarchies dominate. The findings were interpreted through the dual frameworks of cumulative advantage and path dependence, both of which emphasize inertia and reinforcement in scholarly publishing systems.\u003c/p\u003e \u003cp\u003eRegarding RQ1, on the evolution of volumes, citations, and prestige across quartiles, the data reveals a salient stratification. Q1 journals more than doubled their output, growing from an average of fewer than 40 papers per year to over 90, and their citation counts surged sharply (more than 500%). In contrast, Q2 and Q3 outlets expanded at more modest rates, while Q4 journals stagnated, even posting slight declines in average annual volume. Citation counts rose across all quartiles, but the increases were highly uneven, with top-tier journals pulling away from the rest. SJR scores moved upward incrementally across quartiles but without major structural change, confirming the slow-moving character of prestige metrics. The skewness between means and medians highlights that this expansion was driven by a handful of elite outlets rather than broad-based growth. Therefore, in short, the period examined witnessed an entrenchment of stratification, with leading journals consolidating dominance while lower-tier outlets remained marginal.\u003c/p\u003e \u003cp\u003eTurning to RQ2, on short-term relationships between volume growth and contemporaneous changes in impact indicators, the results show that output expansion does not translate into immediate prestige. Correlations between annual changes in document counts and parallel changes in citations or SJR scores were essentially zero, even across nearly 100,000 observations. Median year-to-year changes were minimal which can be understood as strong systematic stability. Quartile mobility patterns reinforce this inertia: leading journals had more than an 80% chance of remaining at the top from one year to the next, while the lowest-ranked outlets had a 61% probability of staying at the bottom. Movement was largely confined to adjacent quartiles, and radical leaps almost never occurred. Even extreme increases in publication volume did not trigger contemporaneous shifts in ranking, underscoring the lagging nature of citation accrual and the resistance of quartile classifications to short-term fluctuations.\u003c/p\u003e \u003cp\u003eFinally, as to RQ3, on the role of historical performance as a conditioning factor of future prestige, the evidence shows that prior standing exerts a far greater influence than changes in output. Previous SJR scores were by far the strongest predictors of quartile placement in subsequent years, with prior citation counts also significant but somewhat less influential. Volume growth contributed positively to regression models, but its effect was modest in comparison, and interaction terms revealed asymmetries. Associations were asymmetric. Volume increases were more strongly related to incremental SJR changes among lower-quartile journals, whereas higher-quartile journals showed larger associations between expansion and citation growth.\u003c/p\u003e \u003cp\u003eTo sum up the results considering the three RQs, the findings establish that the SS journal ecosystem is structurally stable and strongly hierarchical. Growth in output continues across the field but its benefits accrue disproportionately to journals already occupying prestigious positions (see Zhang, 2025). Moreover, expansion may support gradual progress for some outlets, particularly in the middle tiers, yet the overall system is resistant to disruption. Prestige in academic publishing thus remains less a function of short-term scaling and more the outcome of long-standing trajectories, confirming that in the competitive landscape of scholarly communication, history matters as much as present strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Implications and understanding the results in a broader context\u003c/h2\u003e \u003cp\u003eAs with most complex scientometric queries, the question posed in the title of the present paper (\u003cem\u003eWith greater volume comes greater prestige?\u003c/em\u003e) cannot be answered with a simple \u003cem\u003eyes\u003c/em\u003e or \u003cem\u003eno\u003c/em\u003e. The evidence presented here paints a much more compounded picture, one in which expansion of journal output does, indeed, play a role in prestige dynamics, but rarely in the direct, year-to-year manner implied by \u0026lsquo;instant payoff\u0026rsquo; interpretations of prestige metrics. In brief, volume growth contributes, but always in the shadow of entrenched hierarchies, with history and accumulated advantage exerting the decisive influence. Subsequently, prestige, as the findings show, is not something that can be manufactured in the short term through aggressive scaling, but rather something deeply embedded in long-standing trajectories of citation, recognition, and reputation (cf. Zhang, 2025).\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, these results resonate strongly with Merton\u0026rsquo;s enigmatic formulation of the Matthew effect. Journals that already occupy prestigious positions, i.e. those at the top quartile, can leverage their visibility and accumulated capital to turn even modest growth into further gains. They also enjoy disproportionate benefits from expansion (Zhang, 2025) because each additional article is more likely to be cited, more likely to attract submissions from high-status scholars, and more likely to be disseminated through networks that reinforce their dominance. By contrast, lower-quartile journals may expand their volumes substantially, but the relative invisibility of their content and the lack of established citation streams mean that these efforts often translate into negligible changes in prestige metrics. Across models, historical standing (prior SJR and citations) dominates short-term changes in prestige indicators, consistent with cumulative advantage and path dependence. The empirical evidence confirms this inertia. Quartile mobility is overwhelmingly \u0026ldquo;sticky,\u0026rdquo; with Q1 journals maintaining their positions more than 80% of the time and Q4 journals trapped in place more than 60% of the time. Consequently, even when volume surges are dramatic, they do not dislodge the gravitational pull of history.\u003c/p\u003e \u003cp\u003eA critical question, therefore, remains. Are quartiles good representations and is there a need for a change? The abovementioned asymmetry is central to interpreting the findings. For top-quartile journals, scaling output appears relatively \u0026ldquo;safe.\u0026rdquo; From a more critical standpoint, their brand prestige insulates them from the risks of dilution as readers and authors already assume a certain level of quality, and even if the acceptance rate widens, the symbolic value of being published in a Q1 journal remains high. Though previous research revealed journal quality perception gaps and varying opinions on journals prestige (Bryce et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oltheten et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lowe \u0026amp; Locke, 2005; Lowry et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Brooks et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it can be underlined that elite journals falling in the top quartile remain both symbolically and qualitatively elite in terms of scientometric and bibliometric measures. More importantly, these journals sit at the centre of citation networks, which magnifies the impact of additional articles and the potential of receiving higher quality papers (see Ujum et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wakefield, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; West et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McGuigan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Traag, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, in effect, they can convert scale into visibility and citations with greater efficiency than any other quartile. For Q4 journals, however, the picture is bluntly different. Expansion not only fails to produce equivalent returns, but may even backfire by straining editorial quality, undermining selectivity, or flooding the market with articles that remain uncited (see, e.g. Malvić et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maddi \u0026amp; Miotti, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is, thus, claimable that the publication system punishes, or in more conservative terms, marginalizes lower-tier journals that attempt to emulate the strategies of their elite counterparts and in this sense, the Matthew effect is not just cumulative but asymmetric. By this, we mean that the same strategy, in our case, volume growth, produces divergent outcomes depending on where in the hierarchy it is pursued (Zhang, 2025).\u003c/p\u003e \u003cp\u003eThe practical implications are equally telling. For journal editors, the findings of our paper challenge a common assumption that scaling output is a reliable route to upward mobility. While expansion may increase a journal\u0026rsquo;s visibility and provide more opportunities for citation, our analysis shows that such gains are marginal unless accompanied by quality improvements and broader reputational shifts. This is critically important as we have examined that volume growth only converts to increased prestige if there is a strong editorial and strategic background that selectively publish the best, or in other terms, \u0026ldquo;marketable\u0026rdquo; research. For editorial and publishing stakeholders, the results indicate that output expansion alone is weakly related to short-term changes in prestige indicators, and that baseline standing strongly conditions observed associations. This suggests that evaluations of \u0026lsquo;growth strategies\u0026rsquo; should be calibrated to citation-lag dynamics and quartile inertia, and interpreted alongside non-bibliometric factors (e.g., selectivity, scope, editorial practice) that are not observable in SCImago. We understand that this is crucially difficult, especially in the current, highly diluted and overwhelmed editorial process. Nonetheless, prestige-improving strategies must follow a complex and challenging path in order to achieve higher impact \u0026ndash; the growth in volume is just an important step but by far not the only one to take, in this context. For publishers, the message is even more strategic. Scaling portfolios indiscriminately may generate short-term revenue through article processing charges, but it risks undermining prestige, or in more Bourdieusian terms, scholarly capital (cf. Demeter, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) if journals become bloated without corresponding citation impact. Consequently, sustainable prestige strategies, particularly in the crowded landscape of publishing, require a careful balance between volume, visibility, and quality control. Lastly, for policymakers and research evaluators, the findings raise concerns about the heavy reliance on quartile rankings as shorthand measures of quality. This is not a novel critique as mentioned earlier but one that needs for accentuating and investigation. Quartile hierarchies are highly inertial, seemingly resistant to change, and disproportionately reward those already at the top. Thus, by embedding these hierarchies in evaluation systems, policymakers risk reinforcing inequalities between journals, disciplines, and regions, entrenching a publishing ecosystem where mobility is structurally constrained.\u003c/p\u003e \u003cp\u003eThese results can also be situated within the broader literature. Derek de Solla Price\u0026rsquo;s seminal insight into the exponential growth of science continues to hold, but with a crucial caveat where growth does not translate evenly into influence. We find this a pivotal takeaway since the volume of publications may continue to climb incessantly but prestige accrues selectively, clustered around a few dominant nodes in network terms. Demeter\u0026rsquo;s (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) work on global inequalities in publishing resonates here: the expansion of journals in the Global South or in less prestigious disciplines does not automatically translate into upward movement in prestige metrics, largely because the system is oriented around entrenched Western and disciplinary centres. Furthermore, Seglen\u0026rsquo;s (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) critique of journal impact factors also finds empirical support in this study. The SJR quartile system, while more sophisticated than raw impact factors, still exhibits the same slow-moving, path-dependent inertia that makes it a poor indicator of dynamic quality shifts. What we wished to add to these concerns with this study is but a nuance; while volume growth does have measurable, positive associations with prestige, these effects are small, unevenly distributed, and conditioned by historical standing. In other words, growth matters, but it matters differently depending on where a journal starts. The evidence also complicates some optimistic claims in recent literature about the democratizing potential of digitalization and open access (see Frank et al. (2023) who underlines the key flaws of the open access publication trend). It is unarguable that the digitization has, indeed, enabled many journals to increase their volumes by removing the financially challenging constraints of print-publishing, the benefits of this expansion remain heavily skewed. Q1 journals can scale almost without penalty, while Q4 journals risk being perceived as peripheral regardless of their output. The persistence of quartile hierarchies despite the removal of material constraints suggests, therefore, that prestige is less about technological capacity and more about entrenched symbolic capital (Putnam, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This reaffirms Bourdieu\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) argument that academic publishing operates as a field structured by symbolic power, where prestige is accumulated and defended like any other form of capital (also see Demeter, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Roumbanis, 2019; Pellandini-Sim\u0026aacute;nyi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). One of the most critical reflections arising from the study concerns the temporal lag in prestige accumulation. Evidently, citations accrue slowly, SJR score updates gradually, and quartile positions shift only incrementally. However, these progresses make prestige a \u0026ldquo;lagging indicator,\u0026rdquo; one that is resistant to short-term interventions from a holistic perspective. For editors and publishers, this means that even well-executed strategies may take years, if not decades, to bear fruit in the metrics that matter for reputation and for scholars and evaluators, it means that prestige signals often reflect past performance more than present quality. The aforemenioned lag also creates a paradox that is to be considered by all actors in academic knowledge production: the publishing system demands constant innovation and adaptation, yet the rewards are distributed according to historical legacies. In more metaphorical terms, this paradox is akin to running a marathon where the starting lines are historically fixed and where newcomers must keep innovating just to move forward, yet the distance to the leaders is measured not by present speed but by how far ahead they began.\u003c/p\u003e \u003cp\u003eOn a final constructive-critical note, we propose that the findings invite reflection on the normative desirability of such a system. If we were to accept that prestige is path-dependent, cumulative, and exceedingly resistant to change, then the reliance on prestige metrics in research evaluation may not simply reflect quality but actively reproduce inequality. This is supported by the fact that journals at the top continue to accumulate symbolic capital regardless of incremental shifts in quality, while those at the bottom remain \u0026ldquo;stigmatized\u0026rdquo; despite efforts to expand and improve. From this, a form of epistemic injustice emerges where knowledge from peripheral venues is systematically devalued not simply because of its intrinsic merit but because of the structural inertia of the prestige system. We understand that future policy implications are to consider that if quartile rankings and SJR scores continue to serve as dominant proxies for quality, they will reinforce stratification rather than level the playing field, therefore, alternatives that recognize the diversity of scholarly contributions, whether through qualitative assessments, field-sensitive metrics, or broader conceptions of impact, are urgently needed.\u003c/p\u003e \u003cp\u003eIn conclusion, the central takeaway of this study is that prestige in academic publishing cannot be engineered through volume alone. Answering the premise, therefore, would look something akin to this: \u003cem\u003egreater volume may bring prestige, but only when layered on top of an already favorable historical trajectory.\u003c/em\u003e Thus, greater output may support growth in influence for some journals, but it is no guarantee of upward mobility in a system governed by historical inertia. The Matthew effect ensures that those at the top continue to pull away, while those at the bottom struggle to climb. Path dependence cements these patterns, making prestige less a function of current performance and more an echo of past trajectories.\u003c/p\u003e \u003cp\u003eThis study is subject to several limitations that temper the interpretation of its findings. First, although the dataset is extensive, it relies exclusively on SCImago (derived from Scopus) and aggregates 24 heterogeneous subject categories under the umbrella of \u0026ldquo;Social Sciences.\u0026rdquo; This approach provides a valuable \u0026ldquo;big picture,\u0026rdquo; but it inevitably obscures disciplinary specificities, such as, for instance, psychology\u0026rsquo;s faster citation cycles compared to law\u0026rsquo;s slower-moving traditions. Second, the reliance on SCImago\u0026rsquo;s SJR and quartile classifications introduces measurement constraints. Quartiles are relative positions within category-year distributions and shift with changes in the underlying journal pool, while SJR score is a slow-moving, network-weighted indicator. Moreover, the use of a three-year citation window privileges recent surges and disadvantages journals in fields where impact accrues more gradually. In this regard, a further limitation arises from the use of the \u0026ldquo;Best Q\u0026rdquo; rule, which assigns journals the highest quartile they achieve across multiple subject categories. While simplifying analysis, this may artificially inflate the standing of multidisciplinary journals and confound the interpretation of quartile mobility, which might in part reflect reclassification rather than genuine changes in prestige. Methodologically, the use of a gap-tolerant lag\u0026ndash;lead structure to connect years with missing data maintains longitudinal coverage but risks misrepresenting short-term fluctuations when journals reappear after lapses in indexing or inactivity. The modelling strategy also remains associational rather than causal: regression and Markov analyses identify statistical patterns but cannot disentangle whether volume growth drives prestige, or whether already-prestigious journals expand because they can. Important confounding factors, such as editorial selectivity, topical novelty, acceptance rates, language of publication, open access models, and publisher policies, were not included but may critically shape outcomes. Finally, the dataset was harvested in August 2025, but the analytic window ended in 2024; because SCImago periodically updates its backfiles, later replications may yield slightly different slopes, distributions, or transition probabilities. Taken together, these limitations do not undermine the broad conclusions about path dependence and stratification, but they do suggest that the results should be interpreted with diligence and supplemented by future work at finer disciplinary scales, with more diverse indicators, and using designs better suited to distinguishing compositional change, lag structure, and within-journal dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGFL and ZsK wrote the main manuscript text. GFL prepared the analysis and the figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, J.: The fourth age of research. 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Scientometrics. \u003cb\u003e126\u003c/b\u003e, 863\u0026ndash;869 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11192-020-03736-7\u003c/span\u003e\u003cspan address=\"10.1007/s11192-020-03736-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"journal prestige, scientometrics, bibliometrics, publication volume, social sciences, cumulative advantage, path dependence","lastPublishedDoi":"10.21203/rs.3.rs-8745328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8745328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines whether expansion in journal publication volume translates into higher prestige within the social sciences, a field where citation cycles are long and hierarchies persistent. Drawing on Derek de Solla Price\u0026rsquo;s insights into growth, the analysis problematizes the assumption that \u0026ldquo;more is better\u0026rdquo; by asking how volumes, citations, and prestige metrics evolved across quartiles (between 2004\u0026ndash;2024), whether short-term increases in output yield contemporaneous gains in citations, SJR scores, or quartile mobility, and to what extent historical performance mediates these relationships. Using SCImago data, the study employs descriptive trend analysis, correlation tests, regression models, Markov chains, and clustering to try and answer the above questions in the context of structural and dynamic publication patterns. Results show steep stratification. Q1 journals doubled output and gained over 500% in citations, while Q4 stagnated in volume despite growing in number. Short-term volume increases had minor effects on prestige metrics, with correlations near zero and high quartile inertia, namely, that Q1 journals retained rank 84% of the time, Q4 remained stuck 61%. Historical SJR scores and citations may be referenced to predict future prestige while volume growth offered only modest and often asymmetric benefits which supported upward mobility mainly for lower-tier journals.\u003c/p\u003e","manuscriptTitle":"With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 10:29:00","doi":"10.21203/rs.3.rs-8745328/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2840879f-19d1-49b3-aa94-50c605227dfc","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:41:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 10:29:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8745328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8745328","identity":"rs-8745328","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}