Evaluating Existing Approaches to Text Reuse Through an Analysis of Matteo Ricci’s Reliance on Classical Chinese Texts

Evaluating Existing Approaches to Text Reuse Through an Analysis of Matteo Ricci’s Reliance on Classical Chinese Texts | 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 Evaluating Existing Approaches to Text Reuse Through an Analysis of Matteo Ricci’s Reliance on Classical Chinese Texts Stuart Michael McManus, Yuji Li, Leo Tam, Shuyang Qiu, Songyu Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4471619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2025 Read the published version in International Journal of Digital Humanities → Version 1 posted 4 You are reading this latest preprint version Abstract This article analyses the debt to classical Sinographic literature, history and philosophy in the works of the famous Jesuit missionary to the Wanli Emperor, Matteo Ricci (1552-1610). To do so, we trial three recently developed models for analyzing historical text reuse and choose the model best suited to our task. After identifying relevant examples of text reuse in Ricci’s eight surviving works (two of which show particularly high patterns of reuse), we then explore the possible historical factors behind these patterns and subject the citation frequencies of particular classical works, historical periods and genres to analysis using linear and nonlinear regression models. In the end, we conclude that Ricci’s oeuvre borrows heavily from works generally categorized as “Confucian” from the Warring States period, as well as poetic texts of various sorts. This reflects the existing consensus in the historiography, while adding important granular detail. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 Figure 24 Figure 25 1 Introduction Alongside other learned Italian and Portuguese Jesuits, like his companion Michele Ruggieri (1543-1607), Matteo Ricci (1552-1610) crossed Eurasia with the aim of converting the Ming Great State to Christianity. To his formidable humanist education and training in mathematics and astronomy, he succeeded after many years of dedicated study to adding a profound knowledge of the Sinographic tradition. Working in tandem with a Chinese Christian named Xu Guangqi (徐光啓), he used his newfound abilities to undertake groundbreaking works of cross-cultural scholarship, including the first translation of Euclid’s Elements into literary Sintic (Classical Chinese). He also composed a range of works that sought to introduce Christian learning (in the widest possible sense of the word) to Ming literati. Many of these works were reprinted numerous times in the following centuries. By dint of his knowledge and appreciation of the region’s rich humanistic traditions as well as his ability to repackage unfamiliar ideas into forms accessible to the Ming elite, Ricci earned the respect of his readers and contributed to the growing presence of Catholicism in the empire (Hsia, 2016). But which canonical Sinographic texts did Ricci marshal to convince his audiences of the value of Christian learning and thereby the Christian religion? In addition to general studies of his knowledge of the Chinese classics and attempts at creating a Confucian-Christian synthesis (Mungello 1985, 55-73; Ferrero 2019), there are focused studies of his individual compositions (Hosne 2014; Ricci 1985). Yet, there is no comprehensive and systematic study of the role played by canonical Sinographic texts across Ricci’s oeuvre. The most thorough and reliable way to do this is to employ the text reuse method. In the case of classical Chinese, there have been several attempts to build text reuse models. The earliest of these (Sturgeon 2018) is tied to the popular CText (Chinese Text Project) platform, while in the last few years the burgeoning field of digital sinology has offered two additional models (Vierthaler and Gelein 2019; Tharsen and Gladstone 2022). However, neither has there been a third-party assessment of these different models in action, nor have they been applied to the particular case of Matteo Ricci. This article therefore aims to create a corpus of Ricci’s surviving works and apply different models for detecting text reuse to it to ascertain which texts Ricci statistically reused most frequently. Using various regression models, we will then explore whether there is a correlation between Ricci’s works and a number of characteristics already marked in the existing corpora of classical Chinese texts, such as period and genre. 1.1 Historiography Ricci’s missionary enterprise in China has been the subject of extensive research almost since news of it reached the outside world. One frequently emphasized element of Ricci’s approach was his readiness to combine Christianity with elements of the local cultural context, perhaps best exemplified by his efforts to synthesize Christianity and what is generally called “Confucianism” in the Anglophone literature, that is to say the longstanding but malleable amalgam of humanistic traditions, ritual and religious beliefs often called ru (儒) that is perhaps best analogized to Renaissance Humanism or other forms of classicism (Fontana 2011; Goodman and Grafton 1990; Standaert 1999). Rather than merely appreciating and referencing Confucianism in his works, Ricci filled interpretative gaps and demonstrated its compatibility with Catholicism in an Aristotelian mode (Ji 2014). At the same time, Ricci aimed to impress his Chinese contemporaries with his knowledge of esoteric memory techniques, mathematics, science, astronomy and philosophy brought from the Great West ( daxi , 大西) as a way to whet their appetite for Catholicism (Spence 1984). As Ricci’s foremost modern biographer pithily concluded, Ricci aimed to “advance the cause of Christianity by arguing for the harmony and synergy between the western faith and Chinese philosophy” (Hsia, 2016, p. 29). In the early twentieth century, this was dubbed his “accommodation” strategy, borrowing a term from modern Catholic missiology (McManus 2021, p. 114, n. 3). Historians and philologists have also emphasized his debt to ancient Mediterranean texts, especially in his collection of aphorisms On Friendship ( Jiaoyoulun , 交友論), which translated dicta from Plato’s Lysis , Cicero’s On Friendship and Saint Augustine’s Confessions , while also echoing canonical Sinographic works, such as the Book of Songs ( Shijing , 詩經), The Analects ( Lunyu , 論語) and Book of Rituals ( Liji , 禮記) (Zou, 2001). In the same vein, scholars have long emphasized Ricci’s choice of terminology, such as “Lord on High” ( Shangdi ,上帝), “Heaven” (天 Tian ) and “Lord of Heaven” ( Tianzhu , 天主) for the Christian god and his emphasis on “The Way” ( dao , 道) as an expression of a desire to make unfamiliar ideas seem familiar to his learned audience (Fontana, 2011; Ji, 2014, Harbsmeier 2018). All this chimes with the conclusions of the modern Jesuit missionary and scholar Christopher Splatin, S.J. (1975) who similarly argued that Ricci drew heavily on a Confucian-soaked interpretation of Epictetus’ important Stoic work, the Encheiridion , in his little-known Book of 25 Paragraphs ( Er Shi Wu Yan, 二十五言 ) . Generally speaking, Ricci’s efforts were well-received by the Ming elite. His works were reprinted and were widely diffused (Zou, 2001; Witek, 2015). Notable scholars of the period, such as the calligrapher and essayist Chen Jiru (陳繼儒), hailed Ricci as an inspiration for their own works (Zou, 2001), while he also attracted a number of learned collaborators, including Xu Guangqi (徐光啓), Li Zhizao (李之藻), and Yang Tingyun (楊廷筠), collectively known as the “Three Pillars of Chinese Catholicism” ( Shengjiao san zhushi , 聖教三柱石) (Goodman and Grafton 1990; Witek 2015; Liu 2015). As a result, Ricci’s legacy endured in Ming and later Qing China long after his death in 1610. Not only did he pave the way for later Jesuit missionaries (Hsia, 2016), but his name became a byword for the correct approach to spreading Christianity, with several of his works even translated into the Qing’s native Manchu (Witek, 2015). While the Jesuits would later fall from favor at court and eventually be suppressed by Pope Clement XIV in 1773, Ricci remained a symbol of how visitors from the Great West should approach spreading their religion (Fontana, 2011). 2 Corpora 2.1 Matteo Ricci’s Texts We analyzed eight surviving texts authored by Ricci, namely: 1. The True Meaning of the Lord of Heaven (Tianzhu shiyi , 天主實義 ) , 1603 2. Occidental Mnemonics (Xiguo jifa , 西國紀法 ) , 1596 3. On Friendship (Jiaoyou lun , 交友論 ) , 1595 4. The Miracle of Western Letters ( Xizi qiji , 西字奇蹟 ) , 1605 5. Book of 25 Paragraphs ( Ershi wuyan, 二十五言 ) , 1604 6. Introduction to the Chinese translation of Euclid’s Elements (Yi jihe yuanben yin , 譯幾何原本引 ) , 1607 7. Ten Discourses on the Man of Paradox ( Jiren shi pian , 畸人十篇 ) , 1608 8. Testament in Defense of the Faith ( Bian Xue Yi Du, 辨學遺牘 ) , 1624 [published posthumously] Title 天主實義 西國紀法 交友論 西字奇蹟 二十五言 譯幾何原本引 重刻畸人十篇 辨學遺牘 characters 56308 12844 4049 1498 3685 3008 37127 11863 Table 1 For reference, in Table 1 we provide the number of characters in each text and note that The True Meaning of the Lord of Heaven is the longest text, while The Miracle of Western Letters is the shortest. The difference between them amounts to approximately 3,700%. This significant contrast suggests that there is likely to be an unbalanced distribution of text reuse across these eight texts. Since the digital texts available online are of an uneven quality, we referred to recent transcriptions and facsimile editions of his text (making sure to request permission from the press for this purpose). We then OCR’d the texts using the CZUR M3000 Plus Book Scanner. This produced a largely accurate text, except in the case of rare characters, such as 顙 ( sang , “forehead”) and 讎 ( chou , “feud”). Supplementing the scanner’s propriety app with an aptly named tool called GJ.cool (https://ocr.gj.cool/api_demo) produced better results, as it is specifically designed for recognizing classical Chinese characters. Overall, both the CZUR Scanner app and GJ.cool achieved an accuracy rate of over 90%, and any remaining errors were manually corrected. We have since uploaded the texts to CText, so that they can be used by the scholarly community at large. 2.2 Corpus Used for Comparison Due to the restrictions in the model, we had to divide texts into two separate sets for different models. The first corpus comprises all recognized texts from the Pre-Qin and Han dynasties, as well as the Post-Han period, sourced from CText. However, texts from the Qing dynasty were excluded as they were written after Ricci’s time. This initial corpus consists of 132 texts, totaling approximately 20,000,000 characters. For the second corpus, we utilized the database from Kanripo (https://www.kanripo.org/). The Kanripo database categorizes its data into six major genres: Classics (經, jing ), History (史, shi ), Philosophy (子, zi ), Miscellaneous Works (集, ji ), Taoism (道, dao ), and Buddhism (佛, fo ). Within these broad genres, the texts are further divided into numerous subcategories. This diverse corpus enables us to effectively analyze text reuse with reference to valuable metadata. Although the Kanripo corpus is substantially larger, some texts are only found in the CText corpus. Therefore, we combined both corpora to create a larger corpus containing approximately 600,000,000 characters. We selected these two corpora for several reasons. Firstly, they were utilized as demo corpora for our chosen models and have proven to be effective. Secondly, they are easily accessible and available for free download. Lastly, the collection of texts in these corpora is more comprehensive and of higher quality, ensuring the correctness and clean formatting of the texts. 3 Models for Text Reuse For text reuse detection, we employed three different models: 1. CText Text Tool which can be accessed online through the plugin “Text Tools” on CText (https://ctext.org/plugins/texttools/) (Sturgeon 2018). 2. TextPAIR Viewer (TPV) which requires users to run it on their computing units (Tharsen and Gladstone 2022). 3. A BLAST-Based Text Reuse Algorithm which requires users to run it on their computing units (Vierthaler and Gelein 2019). 3.1 Model 1 Model 1 is implemented as a plugin called “Text Tools” within CText and is conveniently accessible online. We utilized the default settings, where the n-gram (i.e. the number of consecutive characters considered as a unit), was set to 4, which we believe is also suitable for our corpus. By inputting texts from CText into the “Similarity” function, we obtained the results easily. The output includes matched quotes, a chapter summary presented as a table, and a similarity matrix. The chapter summary can be converted into a graph. 3.2 Model 2 Model 2 is implemented in Python and runs within a Docker environment. Users have two options for input data types: TEI format (default) or plain text files. In our case, we chose to convert the entire corpus into default TEI format for easy of running. There are several settings that we found impactful to the results. Parameters Value ngram 2 matching_window_size 30 minimum_matching_ngrams_in_window 2 minimum_matching_ngrams 2 Table 2 In Table 2, we show Model 2’s default settings, including an n-gram value of 2. Intuitively, this seemed far too low, so by trial and error we again set it to 5 to generate a higher number of more reliable results. According to the documentation, the matching_window_size refers to the initial evaluation window size for the sequence aligner, while the minimum_matching_ngrams represents the minimum number of matching n-grams required for a match. Although the parameters may seem loose in identifying matching quotes since it includes some very short sentences, the output quality nonetheless remains high. When attempting to decrease these parameters, the output becomes noisy, and some invalid results are displayed. The output includes a website displaying aligned matched quotes and a graph visualizing the results, which includes 43 matching quote records. 3.3 Model 3 This particular model offers seven Python scripts specifically designed for text reuse detection. Each script performs various tasks, such as corpus preparation, text reuse detection, result filtering, and visualization. These scripts are intended to be executed within a Linux environment. The demo corpus used for this model is Kanripo, which aligns well with our corpus. To utilize the model, we simply need to input our corpus and specify the eight texts for comparison. The crucial parameters for Model 3 are provided in Table 3 below. Parameters Value seedlength 4 threshold 0.8 matchlength 16 scorelimit 64 Table 3 In Model 3, the default seed length, or n-gram, is set to 4. The threshold represents the minimum similarity required for two sentences to be considered a match. In the end, Model 3 generated more than 1000 matching quotes (many more than the 43 matching records in Model 2). To reduce noise in the results, we increased the match length from 10 to 16. Searching for longer sentences can effectively reduce the irrelevant reuses that are either coincidences or common word phrases. The output of Model 3 consists of a text file containing all aligned matched quotes and a .csv file that can be visualized as a graph using the Gephi tool. To enhance clarity, we can set a score limit to determine how many texts are displayed in the graph. When there are numerous matching quotes, the graph can become cluttered and difficult to comprehend. By setting the score limit to 64, only texts with a total quote length greater than 64 characters will appear in the graph. This ensures that the texts shown in the graph have a high degree of correlation to Ricci’s works. 3.4 Model Output Examples As an illustration of the text reuse model’s capabilities, consider the following results obtained from the analysis. Model 3 identified a significant overlap between Mencius’《孟子》and “The True Meaning of the Lord of Heaven”《天主實義》. Both texts contain an identical passage. Mencius ( Mengizi , 孟子): 求則得之，舍則失之，是求有益於得也，求在我者也。 The True Meaning of the Lord of Heaven ( Tianzhu shiyi , 天主實義): 求則得之，舍則失之，是求有益於得也，求在我者也。 This demonstrates the model’s ability to detect instances of text reuse and highlights the shared content between different works. Most of the outputs are similar to this example where two sentences are almost identical varying only in a handful of characters. Thematic discourses of Master Zhu ( Zhuzi yulei , 朱子語類): 十目所視，十手所指」，言「小人閒居為不善」，其不善形於外者不可揜如此。「德潤身，心廣體胖 Occidental Mnemonics (Xiguo jifa , 西國紀法): 十目所視，十手所指，其嚴乎？富潤屋，德潤身，心廣體胖 The above output is from model 2. The model can still recognize the text reuse even if the words are not the same or similar in the middle of the sentence. Such findings showcase the strength of the model in identifying textual connections. 3.5 Advantages and Disadvantages of the Three Models Model 1 performs very well on smaller datasets but has limitations when it comes to comparing multiple texts simultaneously, as it can overload and eventually crash the website. Therefore, comparing the entire CText corpus can be time-consuming. It can detect a reasonable number of text reuses, and its main advantage is its convenience and user-friendly nature. Users only need a CText account, and all the work can be done online without the need for additional software . Figure 1 provides a sample output graph. Here we take The True Meaning of the Lord of Heaven (Tianzhu shiyi , 天主實義 ) and its eight sub-chapters as an example to show the results of Model 1; green ovals mark different sub-chapters, and blue ovals represent reused ancient Chinese documents. The two have different thicknesses. are connected by blue lines, where the thicker the blue line, the more references it has. It is worth noting that the output does not include aligned matched quotes. Instead, it contains a similarity matrix, in which the subtitles in row names and column names use blue as the background color, and reused ancient documents use green as the background color. (Figure 2). While this format may not provide immediate insight into the state of reuse, it can still be valuable for further analysis and investigation. Unfortunately, Model 2 did not perform well in our project. The main issue is the insufficient number of outputs even from a large corpus which is not enough for analysis. Considering the performance of the demo data, it appears that this problem may arise from an incompatible data format. Mistakes during the conversion of the TEI format or limitations in the conversion of Kanripo’s data to other formats might also be contributing factors. [1] However, it is worth noting that this model offers the best in-built visualization capabilities among the three models. It provides a website showcasing all aligned matched quotes with a well-designed interface. We take the citation of the Book of Documents ( Shang shu, 尚書) in the context of The True Meaning of the Lord of Heaven as an example. In the output, the left side represents the entire paragraph from the classical text being cited, while the right side presents the excerpt from Matteo Ricci’s work that references the ancient literature. The matching portions between the two will be highlighted in red, indicating instances of reuse(Figure 3). Moreover, it offers additional features, such as the ability to explore passages by frequency of reuse and view passages on a timeline (Figure 4). Unfortunately, due to the absence of publication years in the Kanripo corpus, we are unable to utilize the timeline function. Nonetheless, the output graph (Figure 5) is clear and user-friendly, allowing for easy interpretation and control. As illustrated in Figure 5, the network visualization provides a clear and user-friendly representation of the shared instances of reused passages across multiple cited texts. The visualization effectively demonstrates that each of Ricci’s three distinct writings generates its own set of associated textual elements and reused spherical “constellations.” These constellations encompass recycled text sourced from various locations within the corpus. The visualization is easily interpretable and allows for convenient exploration and manipulation by users. From a historical and philological perspective, all the models share several common limitations that are shared by many text-reuse detection methods. When two texts reuse the same sentence from a third text, there is a risk of misidentifying it as a reuse between texts one and two. In other words, the models cannot determine which texts serve as the source of the reused sentence. Additionally, as a language literary Sinitic is particularly rich in short phrases and idioms. For instance, when using a 4-gram analysis, frequently used expressions like 天地萬物 ( tian di wan wu ), meaning “everything on earth,” may be detected. These must be borne in mind when considering text reuse in Matteo Ricci’s oeuvre. Model 3 performed the best in our analysis. It provides over a thousand matching quotes, all of which appear to be valid (Figures 6 & 7). The instructions for this model are clear and easy to follow. Additionally, the model provides a text file containing all the matched quotes (Figure 8), including the analyzed works of Matteo Ricci, the cited ancient Chinese documents, the length of repetitive sequences involved in the analysis, and the recorded reuse sequences location and other information. In this case, models 1 and 2 have a better visualization. Overall, our goal is to identify the text reuse patterns in Ricci’s work, and the data itself is more important to us than the visualization. Therefore, Model 3 is the preferred choice for our project. 3.6 Other Models Nowadays, there are numerous models available for detecting text reuse, e.g. GanymedeNil/text2vec-large-chinese (https://huggingface.co/GanymedeNil/text2vec-large-chinese). These models generally perform well in finding similar sentences between two texts rather than between two corpora. However, it’s important to note that in a digital humanities context comparing texts using these models can be time-consuming, as they are not designed with our particular needs in mind. Models 1, 2, and 3, which we have discussed earlier, have been well-developed and utilize their algorithms for text comparison. They incorporate various techniques to reduce the time required for finding text reuses in large corpora. These techniques include data preprocessing, sampling, chunking, and the utilization of stop-word lists. Without these techniques, it would be impractical to use general models available on the internet, as they would take a significant amount of time to output results. Meanwhile, it can be challenging for researchers to develop their code to address this issue. Therefore, it is more efficient and convenient to utilize the developed models that come with complete instructions and guidelines. [1] Unfortunately, the instructions and code provided for this model also contain several errors. 4 Analysis 4.1 Introduction This study aims to examine the attributes of the referenced texts within Matteo Ricci’s writings, encompassing preferences for citation and variations in reuse practices across different texts. Visualizations are employed to facilitate the analysis of these characteristics. Furthermore, we seek to explore the association between the frequency of citations of classical and other texts in Literary Sinitic and their specific attributes, such as the historical dynasty of origin, school of thought, and genre. This analysis is conducted through correlation analysis. Additionally, a diverse range of regression models, including multiple linear regression and regression trees, are utilized to identify the features that exert a more pronounced influence on citation frequency. 4.2 Data Overview The dataset used in this study is derived from similarity comparison data generated by employing the CText text tool plugin and its associated corpus. Following the manual identification and selection of citation entries to enhance reliability and accuracy, missing values were addressed using multiple imputation techniques. Multiple imputation involves estimating missing values based on the observed data, creating multiple plausible imputed datasets. Additionally, entries containing missing data were also removed from the dataset. As a result, the dataset encompasses 45 Chinese texts that were referenced in the works of Matteo Ricci, obtained from the CText corpus. Each text is accompanied by its corresponding features, such as the year of composition, themes, philosophical schools, and other pertinent information. To meet the requirements of subsequent analyses, certain categorical variables were transformed into dummy variables. 4.3 Analysis and Results 4.3.1 Approach for Analysis We first extracted relevant data from the whole dataset according to different analysis requirements. Because the exact genesis of some texts is obscure, some characteristics are missing. Therefore, each time after selection, we first removed the rows which contain missing values. After the data cleaning, we did the classifying and summation to obtain the total number of instances of text reuse about certain classification. Tables 4 and 5 are the data processing results, including total numbers of text reuse in different dynasties and the different schools of thoughts. Moreover, we then counted the reuse times in a single work by Ricci for comparison. We only chose those works which had high numbers of citations. The reason is that if the total number is too small, a small value can still represent a significant percentage. Therefore, the percentage computation may not have a typical meaning and may cause some special value impacts. Table 4 Dynasty Total citation times Citation times in 辯學遺牘 Citation times in 天主實義 Citation times in Other Texts Warring States 38 4 34 0 Spring and Autumn period 31 7 18 6 Qin 24 0 19 5 Eastern Han 23 5 17 1 Western Han 18 1 17 0 Ming 12 1 10 1 Northern Song 8 3 5 0 Yuan 7 0 7 0 Western Zhou 6 1 4 1 Tang 3 1 2 0 Wei and Jin 2 0 2 0 Southern Song 1 0 1 0 Northern and Southern Dynasty 1 0 1 0 Table 5 Schools of Thought Total citation times Citation times in 天主實義 Citation times in 辯學遺牘 Citation times in Other Texts Confucianism 117 94 12 11 Taoism 33 27 5 1 Eclectics Ideology 3 1 2 0 General 3 0 3 0 Legalism 2 2 0 0 Mohism 1 1 0 0 4.3.2 Data Visualization The texts reused range from the Western Han Dynasty ( c . 1100-771 BCE) to the Ming Dynasty (1368–1644 CE) and all the main schools of thought are represented. In Figs. 9 to 15 , we can see that in terms of chronology, the Spring and Autumn period and Warring States were the most highly cited across Ricci’s works. In terms of schools of thought, the texts classed as Confucianism in the metadata are dominant with Taoism also well represented. From longitudinal comparison, we found that there were no significant differences in text reuse across Ricci’s publications. 4.4 Analysis of the Characteristics of Cited Works 4.4.1 Approach to Analysis We primarily analyze the inherent features of the referenced ancient Chinese works to determine the presence of certain preferences in Matteo Ricci’s writings. We focus on the following two aspects: the examination and analysis of the correlation between the features of the referenced works and the writings, and the construction of multiple regression models to identify the most influential features on citation counts. Regarding the examination of correlation, correlation coefficients are primarily used as the main criterion. To analyze feature importance, we employed two common approaches: linear regression and nonlinear regression (regression trees). Categorical variables including features of ancient works were transformed into dummy variables to meet the needs of regression model construction. 4.4.2 Visualization and Statistical Discussion of Results We conducted a correlation analysis on different works by Matteo Ricci and the features of the cited works, including the year of publication, work genre, and philosophical school. In this correlation study, we used the Pearson correlation coefficient. In simple terms, this is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. It ranges from − 1 to 1, where a value of 1 indicates a perfect positive linear correlation, -1 indicates a perfect negative linear correlation, and 0 indicates no linear correlation. Based on the correlation coefficient matrix, we plotted heatmaps as shown in Figs. 16 and 17 to visually represent the relationships between variables. The heatmaps provide a color-coded representation of the correlation values, where darker shades indicate stronger correlations and lighter shades indicate weaker correlations. In Fig. 16 , we visualized the correlations between the works of Matteo Ricci and features of other texts in Literary Sinitic. When the Pearson coefficient between different variables is greater than 0.5, it can be considered as having a certain degree of linear correlation. For example, we can infer a significant correlation between Ricci’s Ten Discourses on the Man of Paradox ( Jiren shi pian , 畸人十篇) and texts that fall into the genre of poetry. However, in Fig. 17 , by analyzing the correlations among various features of Chinese literature, we also observed that there are linear correlations among the features of cited works. These features can influence the accuracy of subsequent linear regression models, and will be treated momentarily. We also constructed Ordinary Least Squares (OLS), Lasso, Ridge Regression, and Support Vector Regression (SVR) models using the characteristics of the reused texts and their citation counts, and analyzed and compared these models to determine which features had a greater impact on the citation situation. To do so, we split the original dataset into a training set and a testing set with a ratio of 1:4. The training set was used to build the regression models, and the predicted values were compared with the actual values in the testing set. The performance of different linear regression models (predicted values vs. actual values) is shown in Fig. 18 . Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) of the training and testing sets were used as evaluation metrics to assess the performance of the regression models. RMSE is a commonly used metric that measures the average magnitude of the residuals or prediction errors, taking into account both the magnitude and direction of the errors. It is calculated by taking the square root of the average of the squared differences between the predicted values and the corresponding true values. RMSE provides a measure of the overall goodness of fit of the model, with lower values indicating better performance. On the other hand, Mean Absolute Error (MAE) is another evaluation metric that measures the average magnitude of the absolute differences between the predicted values and the true values. Unlike RMSE, MAE does not consider the squared differences and only focuses on the absolute differences. MAE provides a measure of the average magnitude of the errors, without considering their direction. Similar to RMSE, lower values of MAE indicate better performance of the regression model (Tianfeng, 2022). The relevant results of the RMSE and MAE for the training and testing sets are shown in Table 6 below. These metrics help us understand how well the regression models were able to predict the target variable and compare the performance of different models. Table 6 Train RMSE Test RMSE Train MAE Test MAE OLS 3.2641 9.4441 1.9961 8.8069 Lasso 3.2786 9.1697 2.139 8.3992 Ridge 4.7944 7.867 3.607 4.7965 SVR 4.6283 8.4776 2.2055 4.4933 It can be observed that OLS and Lasso perform the best. However, it is well-known that there exists a significant issue of multicollinearity among the various features of the cited works, which inevitably adversely affects the accuracy of our linear regression model. To assess the reliability of the linear regression model, significance testing (including F-test and t-test) was employed as the basis for evaluation. Significance testing is a statistical method used to determine the significance of relationships or differences in data. In the context of linear regression models, significance testing helps assess the statistical significance of the model and its coefficients. In this case, the significance tests reveal that the constructed linear regression model, which incorporates information such as the number of citations of classical Chinese texts, the dynasty of composition, genre, and philosophical school, is not accurate. This lack of accuracy may be attributed to the presence of severe multicollinearity among the features of the cited works. Using the most effective Ordinary Least Squares (OLS) model as an example, according to Table 7 , we observed that both the results of the overall significance test (f-test) for the entire model and the significance test of individual coefficients (t-test) indicate that the linear regression model built upon information such as the number of citations of ancient Chinese texts, the dynasty of composition, genre, and philosophy school is not sufficiently precise. OLS Regression Results Dep. Variable: Total R-squared: 0.538 Model: OLS Adj. R-squared: -0.385 Method: Least Squares F-statistic: 0.5829 No. Observations: 28 Prob (F-statistic): 0.842 Df Residuals: 9 Log-Likelihood: -72.854 Df Model: 18 AIC: 183.7 Covariance Type: Nonrobust BIC: 209 Table 7 According to the results of correlation and significance tests, we discovered the presence of significant multicollinearity in the linear regression models using the characteristics of ancient works and their citation counts. To further validate the previous findings and address the issue of multicollinearity, we incorporated regression tree analysis into our methodology. Regression tree analysis is a decision tree-based approach that allows for the identification of important features. In our study, we employed various regression tree techniques, including random forest, Tree with AdaBoost, and Tree with Gradient boosting, to conduct an analysis of the significant features. These techniques aim to capture complex nonlinear relationships and interactions among the variables. For instance, Fig. 19 illustrates the utilization of the regression tree principle. We employed the standard deviation as the criterion for branching in the regression tree. The splitting point was determined based on the calculation of the standard deviation of the target variable, which represents the predicted value, for each branch. The results of the analysis of important features using four regression tree models are shown in Figs. 20 to 23 . Overall, in terms of citation count, the “Warring States” period appears to be the greatest driver. In terms of philosophical schools, the trend known as “Confucianism” appears to be the primary influencing factor. In terms of genre, both “Poetry” and “Philosophy” are prominent. It is worth mentioning that in terms of chronology of the reused texts, the importance of the “Spring and Autumn Period” as an influencing factor is not significantly different from the “Warring States” period. This may be because both periods belong to the “Pre-Qin Period,” a crucial period for the development of philosophy, including the rise of various philosophical schools such as Confucianism, Taoism, and Mohism, all of which continued to play important roles in Ricci’s day. 4.4.3 Explanation and Limitations of Our Analysis Based on the analysis of linear and non-linear regression models presented earlier, we can conclude that Matteo Ricci’s writings exhibit a preference for citing and reworking texts with specific characteristics. To wit, there is a higher tendency to cite works from the Warring States period, works related to Confucianism, and poetic works. This preference can be understood as Matteo Ricci’s search for strong and familiar foundations on which to build his presentation of Christian teachings. As is well known, Ricci’s aligned the Christian God “the Lord of Heaven” ( Tianzhu 天主 ), with a diety frequently mentioned in classical texts called “the Lord Above” ( shangdi 上帝) , as expressed in his mantra “The Lord of Heaven and the Lord Above agree in doctrine” ( Tianzhu Shangdi Tongyilun , 天主上帝同一論) (Goodman and Grafton 1990 ; Ji 2014 ). Ricci faced challenges in his missionary work in China primarily due to the deep-rooted reverence for their own traditions among the ruling class and elites, which often resulted in skepticism and even rejection of Christianity as a destructive influence (Fontana, 2011 ). To achieve his missionary goals, Ricci integrated Christian theories with Confucianism, which held a prominent position in Chinese intellectual circles. He interpreted the God mentioned in ancient texts as the Christian God, appealing to classics such as the Shi Jing (詩經) and the Shu Jing (書經) from the Spring and Autumn and Warring States periods (Ji 2014 ). By doing so, he sought to mitigate the ruling class’s rejection of Christian theories within the context of cultural recognition and reverence for ancient traditions, ultimately facilitating the acceptance of Christianity in China (Goodman and Grafton, 1990 ). Of course, there are limitations to this study, some inherent in its limited scope, others in Ricci’s writings themselves. There is significant variation in the length of Matteo Ricci’s writings, leading to differences in the number of reuses among different works. Some texts may not contain any citations, indirectly affecting the analysis of citation preferences. Different larger corpora of texts in Literary Sinitic might also yield different results due to a range of factors, such as completeness, quality of text pre-processing, etc. We therefore encourage digital sinologists to redo this analysis focusing on an author who has a larger or more varied oeuvre. Ricci is a towering figure in the history of China-Western relations, and so his works are as good a starting point as any. However, it should be repeated across a range of authors for a richer sense of how the 3 models perform and the sort of insights can be gleaned from them. Similarly, different corpora can be used. 4.4.4 Supplementary Analysis of Reuse Practice Using a Larger Corpus To mitigate some of the latter limitations of this study, a supplementary analysis was conducted to further investigate the reuse practice in Matteo Ricci’s works by utilizing a larger corpus, specifically Kanripo. This analysis aims to provide additional insights to complement the experimental results obtained from CText and CText text tool. Due to the large number of results, manual labeling and statistical analysis of all the data proved challenging. However, with the help of Kanripo’s categorization, we were able to analyze the data using visual representations. Figure 24 displays a total of 1,092 results, with 876 stemming from The True Meaning of the Lord of Heaven (天主實義) , 59 from Xiguo Jifa (西國紀法) , 3 from the Translation of Euclid’s Elements (譯幾何原本引) , 138 from Chong Ke Ji Ren Shi Pian (重刻畸人十篇) , 4 from On Friendship (交友論) , 12 from Bian Xue Yi Du (辨學遺牘) , and none from both Xiji Qiji (西字奇蹟) and Er Shi Wu Yan (二十五言) . These numbers align with our expectations based on the lengths of the respective texts. Figure 25 then illustrates the distribution by genre of the total results. 665 are from the Classics, 137 from History, 144 from Philosophy, 103 from Miscellaneous Works, 6 from Taoist works, and 28 from Buddhist works. In Fig. 26 , the genres are further divided into smaller categories, and it is evident that the Documentary (書類) subcategory contributes significantly to the reuses. One notable example within this category is Venerated Documents (Shang shu, 尚書) , also known as The Book of Documents (Shu jing , 書經) , which contains a variety of orations and dialogues by rulers and officials. According to the result, there are 249 instances of text reuse found in Shang Shu . For instance, in The True Meaning of the Lord of Heaven Ricci reused the following two passages from the Shang Shu : 1. “The King Wen of Zhou who is respectful, humble, diligent and striving to serve that God,” (維此文王, 小心翼翼, 昭事上帝). 2. “The Lord God endowed all people with a benevolent nature. However, the one who can truly guide their lives by their inherent goodness and bring stability is none other than the king,” (惟皇上帝降衷于下民, 若有恆性, 克綏厥猷惟后). Here, we see Ricci making efforts to establish a connection between the ancient deity and the God of Catholicism that he presented as one and the same. 5 Conclusion To advance the Jesuit missionary enterprise, Matteo Ricci employed a combination of Christian teachings, Greco-Roman philosophy and authoritative classical Chinese texts in his works. By emphasizing the compatibility between Confucianism and Christianity, Ricci garnered admiration from Ming elites and became a source of inspiration for intellectually curious scholars. However, previous studies have not adopted digital models to analyze Ricci’s test reuse patterns of Chinese works. Hence, this research aimed to fill the gap by digitizing, uploading, and analyzing Matteo Ricci’s compositions in literary Sinitic within the wider context of the Sinographic literary tradition. Ultimately, our analysis chimes with the existing scholarship in showing a preference for citing ancient compositions from the Warring States period, those related to Confucianism, and those with poetic or philosophical topics. In terms of methodology, all three models that we used had their limitations. In the end, two texts reusing the same sentence from another text will be wrongly identified as reuse between them. They have no way to recognize which texts are the source of reuse. For the purposes of this analysis, Model 3 emerged as the most useful. It can generate more results and provides a concrete analysis of Ricci’s reuse. We suggest that digital sinologists use this as a starting point in their own work. Declarations Author Contribution This research was undertaken as a summer project by student members of the Digital History Lab overseen by Prof. Stuart M. McManus who provided the idea for the project. Leo Tam did much of the preliminary analysis and assessment of the 3 models. Yuji Li and Shuyang Qiu worked primarily on the statistical analysis. Songyu Liu and Letian Yu worked on the visualizations and related tasks. As history students, Daniel Ng and Warner Wong assisted with the historical analysis. References Harbsmeier, C. (2018). “Matteo Ricci on Friendship: Some Latin Sources for his Chinese Book.” In Iwo Amelung, Joachim Kurtz (eds.) In Reading the Signs: Philology, History, Prognostication Festschrift for Michael Lackner. München: Iudicium Verlag, 2018. Hosne, A. C. (2014). Friendship among Literati. Matteo Ricci SJ (1552–1610) in Late Ming China. The Journal of Transcultural Studies, 5(1), 190–214. https://doi.org/10.11588/ts.2014.1.11362 Spalatin, C. (1975). Matteo Ricci’s Use of Epictetus’ Encheiridion. Gregorianum , 56(3), 551-557 Spence, J. D. (1984). The Memory Palace of Matteo Ricci . London: Faber and Faber. Sturgeon, D. (2018). Digital Approaches to Text Reuse in the Early Chinese Corpus. Journal of Chinese Literature and Culture, 5(2), 186-213. https://doi.org/10.1215/23290048-7256963 Mungello, D. E. (1985). Curious Land: Jesuit Accommodation and the Origins of Sinology . Stuttgart: F. Steiner Verlag Wiesbaden. Ji, J. (2014). The origin of Sino-Christian Theology: Matteo Ricci’s idea of Shang-ti, T’ien and Translation of ‘Deus’ in Late-Ming China. Sino-Christian Studies , 17(2014): 47-74. Tharsen, J. and Gladstone, C. (2022). “TextPAIR Viewer (TPV) 1.0: An Interactive Visual Toolkit for Exploring Networks of Textual Alignments and Text Reuse.” Shuzi renwen 數字人文 2: https://www.dhcn.cn/site/works/dhjournal/20452.html Goodman, H. L., and Grafton A. (1990). Ricci, the Chinese, and the Toolkits of Textualists. Asia Major , 2, 95-148. Ferrero, M. (2019). “Motivation to Act in Confucianism and Christianity: In Matteo Ricci’s The True Meaning of the Lord of Heaven (Tianzhu Shiyi 天主實義).” Frontiers of philosophy in China 14, no. 2 (2019): 226–247. Fontana, M. (2011). Matteo Ricci: A Jesuit in the Ming Court . Lanham, MD: Rowman & Littlefield Publishers. McManus, S. M. (2021). Empire of Eloquence: The Classical Rhetorical Tradition in Colonial Latin America and the Iberian World . Cambridge: Cambridge University Press. Ricci, M. (1985). The True Meaning of the Lord of Heaven: T’ien-Chu Shih-I Chinese-English ed. Douglas Lancashire, Peter Hu Kuo-chen, and Edward Malatesta. Taipei: Institut Ricci. Standaert, N. 1999. “The Jesuits Did NOT Manufacture ‘Confucianism.’” East Asian Science, Technology, and Medicine , 16, pp. 115–32. Hsia, R. P. (2016) Matteo Ricci and the Catholic Mission to China, 1583-1610: A Short History with Documents. Indianapolis: Hackett Publishing Company. Vierthaler, P. and Gelein, M. (2019). “A BLAST-Based, Language-Agnostic Text Reuse Algorithm with a MARKUS Implementation and Sequence Alignment Optimized for Large Chinese Corpora.” Journal of Cultural Analytics , 4.2. https://doi.org/10.22148/16.034. Chai, T. (2022). Root Mean Square. In: Daya Sagar, B.S., Cheng, Q., McKinley, J., Agterberg, F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_280-1 Witek, J. W. (2015). “Christianity and China: Universal Teaching from the West,” In Uhalley, S,, and Wu, X. (eds) China and Christianity: Burdened Past, Hopeful Future . London: Routledge. Liu, Y. (2015). Harmonious Disagreement: Matteo Ricci and His Closest Chinese Friends . New York: Peter Lang. Zou, Z. 邹振环 (2001). “Limadou ‘Jiaoyoulun’ de yikan yu chuanbo 利玛窦《交友论》的译刊与传播 (Translation and Publication of Matteo Ricci’s On Friendship ).” Fudan Journal (Social Sciences) 3: 49-55. Footnotes Unfortunately, the instructions and code provided for this model also contain several errors. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2025 Read the published version in International Journal of Digital Humanities → Version 1 posted Editorial decision: Revision requested 29 Sep, 2024 Editor assigned by journal 08 Jun, 2024 Submission checks completed at journal 27 May, 2024 First submitted to journal 24 May, 2024 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. 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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-4471619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312045132,"identity":"33bf918f-f075-454f-9399-8c10cf72a76b","order_by":0,"name":"Stuart Michael 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18","display":"","copyAsset":false,"role":"figure","size":51721,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/2b930be611383e1659bcbef5.png"},{"id":58122190,"identity":"0520480e-e2ba-4fad-886c-ee6171ab9654","added_by":"auto","created_at":"2024-06-11 12:20:32","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":31577,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"20.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/c17fb4bda4abe333d97d4cf8.png"},{"id":58122225,"identity":"3fba3e33-93a5-457e-b452-69e0ca982856","added_by":"auto","created_at":"2024-06-11 12:20:33","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":38841,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"21.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/3097cd58b7aabf02fa65114a.png"},{"id":58122230,"identity":"a225df3a-14d6-4b16-a3e5-17e46d2c4b2f","added_by":"auto","created_at":"2024-06-11 12:20:33","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":35367,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/0825fb3ef1444bd6a9696092.png"},{"id":58122219,"identity":"b3824b52-0563-4c32-9557-37a2b65308f3","added_by":"auto","created_at":"2024-06-11 12:20:32","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":34737,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"23.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/4739c63d8dbab02746074eaa.png"},{"id":58122804,"identity":"ca281caa-c4d7-468c-aa47-edcc9384df75","added_by":"auto","created_at":"2024-06-11 12:28:33","extension":"png","order_by":23,"title":"Figure 23","display":"","copyAsset":false,"role":"figure","size":38135,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"24.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/e2bcdaade8b8be1005f69169.png"},{"id":58122224,"identity":"fe951356-5966-47c9-aa9f-a6871f81879f","added_by":"auto","created_at":"2024-06-11 12:20:33","extension":"png","order_by":24,"title":"Figure 24","display":"","copyAsset":false,"role":"figure","size":27958,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"25.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/07b21eb5a44cb5b0791692fe.png"},{"id":58122233,"identity":"44777068-6711-4f88-887c-8b2d547dab04","added_by":"auto","created_at":"2024-06-11 12:20:33","extension":"png","order_by":25,"title":"Figure 25","display":"","copyAsset":false,"role":"figure","size":78385,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"26.png","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/a9492c18b7de609caa3e5634.png"},{"id":86179869,"identity":"5beef7ff-70b3-4d29-87d7-d4d33cf103ad","added_by":"auto","created_at":"2025-07-07 16:20:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5062298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4471619/v1/1e0c10d2-e8cc-41f8-a729-4d8e2db64825.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Existing Approaches to Text Reuse Through an Analysis of Matteo Ricci’s Reliance on Classical Chinese Texts","fulltext":[{"header":" 1 Introduction ","content":"\u003cp\u003eAlongside other learned Italian and Portuguese Jesuits, like his companion Michele Ruggieri (1543-1607), Matteo Ricci (1552-1610) crossed Eurasia with the aim of converting the Ming Great State to Christianity. To his formidable humanist education and training in mathematics and astronomy, he succeeded after many years of dedicated study to adding a profound knowledge of the Sinographic tradition. Working in tandem with a Chinese Christian named Xu Guangqi (徐光啓), he used his newfound abilities to undertake groundbreaking works of cross-cultural scholarship, including the first translation of Euclid\u0026rsquo;s \u003cem\u003eElements\u003c/em\u003e into literary Sintic (Classical Chinese). He also composed a range of works that sought to introduce Christian learning (in the widest possible sense of the word) to Ming literati. Many of these works were reprinted numerous times in the following centuries. By dint of his knowledge and appreciation of the region\u0026rsquo;s rich humanistic traditions as well as his ability to repackage unfamiliar ideas into forms accessible to the Ming elite, Ricci earned the respect of his readers and contributed to the growing presence of Catholicism in the empire (Hsia, 2016).\u003c/p\u003e\n\n\u003cp\u003eBut which canonical Sinographic texts did Ricci marshal to convince his audiences of the value of Christian learning and thereby the Christian religion? In addition to general studies of his knowledge of the Chinese classics and attempts at creating a Confucian-Christian synthesis (Mungello 1985, 55-73; Ferrero 2019), there are focused studies of his individual compositions (Hosne 2014; Ricci 1985). Yet, there is no comprehensive and systematic study of the role played by canonical Sinographic texts across Ricci\u0026rsquo;s oeuvre. \u003c/p\u003e\n\n\u003cp\u003eThe most thorough and reliable way to do this is to employ the text reuse method. In the case of classical Chinese, there have been several attempts to build text reuse models. The earliest of these (Sturgeon 2018) is tied to the popular CText (Chinese Text Project) platform, while in the last few years the burgeoning field of digital sinology has offered two additional models (Vierthaler and Gelein 2019; Tharsen and Gladstone 2022). However, neither has there been a third-party assessment of these different models in action, nor have they been applied to the particular case of Matteo Ricci. This article therefore aims to create a corpus of Ricci\u0026rsquo;s surviving works and apply different models for detecting text reuse to it to ascertain which texts Ricci statistically reused most frequently. Using various regression models, we will then explore whether there is a correlation between Ricci\u0026rsquo;s works and a number of characteristics already marked in the existing corpora of classical Chinese texts, such as period and genre.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.1 Historiography\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eRicci\u0026rsquo;s missionary enterprise in China has been the subject of extensive research almost since news of it reached the outside world. One frequently emphasized element of Ricci\u0026rsquo;s approach was his readiness to combine Christianity with elements of the local cultural context, perhaps best exemplified by his efforts to synthesize Christianity and what is generally called \u0026ldquo;Confucianism\u0026rdquo; in the Anglophone literature, that is to say the longstanding but malleable amalgam of humanistic traditions, ritual and religious beliefs often called \u003cem\u003eru\u003c/em\u003e (儒) that is perhaps best analogized to Renaissance Humanism or other forms of classicism (Fontana 2011; Goodman and Grafton 1990; Standaert 1999). Rather than merely appreciating and referencing Confucianism in his works, Ricci filled interpretative gaps and demonstrated its compatibility with Catholicism in an Aristotelian mode (Ji 2014). At the same time, Ricci aimed to impress his Chinese contemporaries with his knowledge of esoteric memory techniques, mathematics, science, astronomy and philosophy brought from the Great West (\u003cem\u003edaxi\u003c/em\u003e, 大西) as a way to whet their appetite for Catholicism (Spence 1984). As Ricci\u0026rsquo;s foremost modern biographer pithily concluded, Ricci aimed to \u0026ldquo;advance the cause of Christianity by arguing for the harmony and synergy between the western faith and Chinese philosophy\u0026rdquo; (Hsia, 2016, p. 29). In the early twentieth century, this was dubbed his \u0026ldquo;accommodation\u0026rdquo; strategy, borrowing a term from modern Catholic missiology (McManus 2021, p. 114, n. 3). \u003c/p\u003e\n\n\u003cp\u003eHistorians and philologists have also emphasized his debt to ancient Mediterranean texts, especially in his collection of aphorisms \u003cem\u003eOn Friendship \u003c/em\u003e(\u003cem\u003eJiaoyoulun\u003c/em\u003e, 交友論), which translated dicta from Plato\u0026rsquo;s \u003cem\u003eLysis\u003c/em\u003e, Cicero\u0026rsquo;s \u003cem\u003eOn Friendship\u003c/em\u003e and Saint Augustine\u0026rsquo;s \u003cem\u003eConfessions\u003c/em\u003e, while also echoing canonical Sinographic works, such as the \u003cem\u003eBook of Songs\u003c/em\u003e (\u003cem\u003eShijing\u003c/em\u003e, 詩經), \u003cem\u003eThe Analects\u003c/em\u003e (\u003cem\u003eLunyu\u003c/em\u003e, 論語) and \u003cem\u003eBook of Rituals\u003c/em\u003e (\u003cem\u003eLiji\u003c/em\u003e, 禮記) (Zou, 2001). In the same vein, scholars have long emphasized Ricci\u0026rsquo;s choice of terminology, such as \u0026ldquo;Lord on High\u0026rdquo; (\u003cem\u003eShangdi\u003c/em\u003e,上帝), \u0026ldquo;Heaven\u0026rdquo; (天 \u003cem\u003eTian\u003c/em\u003e) and \u0026ldquo;Lord of Heaven\u0026rdquo; (\u003cem\u003eTianzhu\u003c/em\u003e, 天主) for the Christian god and his emphasis on \u0026ldquo;The Way\u0026rdquo; (\u003cem\u003edao\u003c/em\u003e, 道) as an expression of a desire to make unfamiliar ideas seem familiar to his learned audience (Fontana, 2011; Ji, 2014, Harbsmeier 2018). All this chimes with the conclusions of the modern Jesuit missionary and scholar Christopher Splatin, S.J. (1975) who similarly argued that Ricci drew heavily on a Confucian-soaked interpretation of Epictetus\u0026rsquo; important Stoic work, the \u003cem\u003eEncheiridion\u003c/em\u003e, in his little-known \u003cem\u003eBook of 25 Paragraphs \u003c/em\u003e(\u003cem\u003eEr Shi Wu Yan, \u003c/em\u003e\u003cem\u003e二十五言\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e. \u003c/p\u003e\n\u003cp\u003eGenerally speaking, Ricci\u0026rsquo;s efforts were well-received by the Ming elite. His works were reprinted and were widely diffused (Zou, 2001; Witek, 2015). Notable scholars of the period, such as the calligrapher and essayist Chen Jiru (陳繼儒), hailed Ricci as an inspiration for their own works (Zou, 2001), while he also attracted a number of learned collaborators, including Xu Guangqi (徐光啓), Li Zhizao (李之藻), and Yang Tingyun (楊廷筠), collectively known as the \u0026ldquo;Three Pillars of Chinese Catholicism\u0026rdquo; (\u003cem\u003eShengjiao san zhushi\u003c/em\u003e, 聖教三柱石) (Goodman and Grafton 1990; Witek 2015; Liu 2015).\u003c/p\u003e\n\n\u003cp\u003eAs a result, Ricci\u0026rsquo;s legacy endured in Ming and later Qing China long after his death in 1610. Not only did he pave the way for later Jesuit missionaries (Hsia, 2016), but his name became a byword for the correct approach to spreading Christianity, with several of his works even translated into the Qing\u0026rsquo;s native Manchu (Witek, 2015). While the Jesuits would later fall from favor at court and eventually be suppressed by Pope Clement XIV in 1773, Ricci remained a symbol of how visitors from the Great West should approach spreading their religion (Fontana, 2011). \u003c/p\u003e\n"},{"header":"2 Corpora","content":"\u003cp\u003e\u003cstrong\u003e2.1 Matteo Ricci\u0026rsquo;s Texts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed eight surviving texts authored by Ricci, namely:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u003cem\u003eThe True Meaning of the Lord of Heaven (Tianzhu shiyi\u003c/em\u003e,\u0026nbsp;\u003cem\u003e天主實義\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1603\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u003cem\u003eOccidental Mnemonics\u003c/em\u003e \u003cem\u003e(Xiguo jifa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e西國紀法\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1596\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u003cem\u003eOn Friendship (Jiaoyou lun\u003c/em\u003e,\u0026nbsp;\u003cem\u003e交友論\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1595\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u003cem\u003eThe Miracle of Western Letters\u0026nbsp;\u003c/em\u003e(\u003cem\u003eXizi qiji\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e西字奇蹟\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1605\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u003cem\u003eBook of 25 Paragraphs\u0026nbsp;\u003c/em\u003e(\u003cem\u003eErshi wuyan,\u0026nbsp;\u003c/em\u003e\u003cem\u003e二十五言\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1604\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;\u003cem\u003eIntroduction to the Chinese translation of Euclid\u0026rsquo;s Elements (Yi jihe yuanben yin\u003c/em\u003e,\u0026nbsp;\u003cem\u003e譯幾何原本引\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1607\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp;\u003cem\u003eTen Discourses on the Man of Paradox\u003c/em\u003e (\u003cem\u003eJiren shi pian\u003c/em\u003e,\u0026nbsp;\u003cem\u003e畸人十篇\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1608\u003c/p\u003e\n\u003cp\u003e8. \u0026nbsp; \u003cem\u003eTestament in Defense of the Faith\u0026nbsp;\u003c/em\u003e(\u003cem\u003eBian Xue Yi Du,\u0026nbsp;\u003c/em\u003e\u003cem\u003e辨學遺牘\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e, 1624 [published posthumously]\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e天主實義\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e西國紀法\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e交友論\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e西字奇蹟\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e二十五言\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e譯幾何原本引\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e重刻畸人十篇\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e辨學遺牘\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003echaracters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable\u0026nbsp;1\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor reference, in Table 1 we provide the number of characters in each text and note that \u003cem\u003eThe True Meaning of the Lord of Heaven\u003c/em\u003e is the longest text, while \u003cem\u003eThe Miracle of Western Letters\u0026nbsp;\u003c/em\u003eis the shortest. The difference between them amounts to approximately 3,700%. This significant contrast suggests that there is likely to be an unbalanced distribution of text reuse across these eight texts.\u003c/p\u003e\n\u003cp\u003eSince the digital texts available online are of an uneven quality, we referred to recent transcriptions and facsimile editions of his text (making sure to request permission from the press for this purpose). We then OCR\u0026rsquo;d the texts using the CZUR M3000 Plus Book Scanner. This produced a largely accurate text, except in the case of rare characters, such as\u0026nbsp;顙\u0026nbsp;(\u003cem\u003esang\u003c/em\u003e, \u0026ldquo;forehead\u0026rdquo;)\u0026nbsp;and\u0026nbsp;讎\u0026nbsp;(\u003cem\u003echou\u003c/em\u003e, \u0026ldquo;feud\u0026rdquo;). Supplementing the scanner\u0026rsquo;s propriety app with an aptly named tool called GJ.cool (https://ocr.gj.cool/api_demo) produced better results, as it is specifically designed for recognizing classical Chinese characters. Overall, both the CZUR Scanner app and GJ.cool achieved an accuracy rate of over 90%, and any remaining errors were manually corrected. We have since uploaded the texts to CText, so that they can be used by the scholarly community at large.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Corpus Used for Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictions in the model, we had to divide texts into two separate sets for different models. The first corpus comprises all recognized texts from the Pre-Qin and Han dynasties, as well as the Post-Han period, sourced from CText. However, texts from the Qing dynasty were excluded as they were written after Ricci\u0026rsquo;s time. This initial corpus consists of 132 texts, totaling approximately 20,000,000 characters.\u003c/p\u003e\n\u003cp\u003eFor the second corpus, we utilized the database from Kanripo (https://www.kanripo.org/). The Kanripo database categorizes its data into six major genres: Classics (經, \u003cem\u003ejing\u003c/em\u003e), History (史, \u003cem\u003eshi\u003c/em\u003e), Philosophy (子, \u003cem\u003ezi\u003c/em\u003e), Miscellaneous Works (集, \u003cem\u003eji\u003c/em\u003e), Taoism (道, \u003cem\u003edao\u003c/em\u003e), and Buddhism (佛, \u003cem\u003efo\u003c/em\u003e). Within these broad genres, the texts are further divided into numerous subcategories. This diverse corpus enables us to effectively analyze text reuse with reference to valuable metadata. Although the Kanripo corpus is substantially larger, some texts are only found in the CText corpus. Therefore, we combined both corpora to create a larger corpus containing approximately 600,000,000 characters. We selected these two corpora for several reasons. Firstly, they were utilized as demo corpora for our chosen models and have proven to be effective. Secondly, they are easily accessible and available for free download. Lastly, the collection of texts in these corpora is more comprehensive and of higher quality, ensuring the correctness and clean formatting of the texts.\u003c/p\u003e"},{"header":"3 Models for Text Reuse","content":"\u003cp\u003eFor text reuse detection, we employed three different models:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; CText Text Tool which can be accessed online through the plugin \u0026ldquo;Text Tools\u0026rdquo; on CText (https://ctext.org/plugins/texttools/) (Sturgeon 2018).\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; TextPAIR Viewer (TPV) which requires users to run it on their computing units (Tharsen and Gladstone 2022).\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; A BLAST-Based Text Reuse Algorithm which requires users to run it on their computing units (Vierthaler and Gelein 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Model 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 1 is implemented as a plugin called \u0026ldquo;Text Tools\u0026rdquo; within CText and is conveniently accessible online. We utilized the default settings, where the n-gram (i.e. the number of consecutive characters considered as a unit), was set to 4, which we believe is also suitable for our corpus. By inputting texts from CText into the \u0026ldquo;Similarity\u0026rdquo; function, we obtained the results easily. The output includes matched quotes, a chapter summary presented as a table, and a similarity matrix. The chapter summary can be converted into a graph.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Model 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 2 is implemented in Python and runs within a Docker environment. Users have two options for input data types: TEI format (default) or plain text files. In our case, we chose to convert the entire corpus into default TEI format\u0026nbsp;for easy of running. There are several settings that we found impactful to the results.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003engram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ematching_window_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eminimum_matching_ngrams_in_window\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eminimum_matching_ngrams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2\u003c/p\u003e\n\u003cp\u003eIn Table 2, we show Model 2\u0026rsquo;s default settings, including an n-gram value of 2. Intuitively, this seemed far too low, so by trial and error we again set it to 5 to generate a higher number of more reliable results. According to the documentation, the matching_window_size refers to the initial evaluation window size for the sequence aligner, while the minimum_matching_ngrams represents the minimum number of matching n-grams required for a match. Although the parameters may seem loose in identifying matching quotes since it includes some very short sentences, the output quality nonetheless remains high. When attempting to decrease these parameters, the output becomes noisy, and some invalid results are displayed. The output includes a website displaying aligned matched quotes and a graph visualizing the results, which includes 43 matching quote records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Model 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis particular model offers seven Python scripts specifically designed for text reuse detection. Each script performs various tasks, such as corpus preparation, text reuse detection, result filtering, and visualization. These scripts are intended to be executed within a Linux environment. The demo corpus used for this model is Kanripo, which aligns well with our corpus. To utilize the model, we simply need to input our corpus and specify the eight texts for comparison. The crucial parameters for Model 3 are provided in Table 3 below.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eseedlength\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ethreshold\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ematchlength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003escorelimit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3\u003c/p\u003e\n\u003cp\u003eIn Model 3, the default seed length, or n-gram, is set to 4. The threshold represents the minimum similarity required for two sentences to be considered a match. In the end, Model 3 generated more than 1000 matching quotes (many more than the 43 matching records in Model 2). To reduce noise in the results, we increased the match length from 10 to 16. Searching for longer sentences can effectively reduce the irrelevant reuses that are either coincidences or common word phrases.\u003c/p\u003e\n\u003cp\u003eThe output of Model 3 consists of a text file containing all aligned matched quotes and a .csv file that can be visualized as a graph using the Gephi tool. To enhance clarity, we can set a score limit to determine how many texts are displayed in the graph. When there are numerous matching quotes, the graph can become cluttered and difficult to comprehend. By setting the score limit to 64, only texts with a total quote length greater than 64 characters will appear in the graph. This ensures that the texts shown in the graph have a high degree of correlation to Ricci\u0026rsquo;s works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Model Output Examples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs an illustration of the text reuse model\u0026rsquo;s capabilities, consider the following results obtained from the analysis. Model 3 identified a significant overlap between Mencius\u0026rsquo;《孟子》and \u0026ldquo;The True Meaning of the Lord of Heaven\u0026rdquo;《天主實義》. Both texts contain an identical passage.\u003c/p\u003e\n\u003cp\u003eMencius\u0026nbsp;(\u003cem\u003eMengizi\u003c/em\u003e, 孟子):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e求則得之，舍則失之，是求有益於得也，求在我者也。\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe True Meaning of the Lord of Heaven\u0026nbsp;\u003c/em\u003e(\u003cem\u003eTianzhu shiyi\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e天主實義):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e求則得之，舍則失之，是求有益於得也，求在我者也。\u003c/p\u003e\n\u003cp\u003eThis demonstrates the model\u0026rsquo;s ability to detect instances of text reuse and highlights the shared content between different works. Most of the outputs are similar to this example where two sentences are almost identical varying only in a handful of characters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThematic discourses of Master Zhu\u003c/em\u003e (\u003cem\u003eZhuzi yulei\u003c/em\u003e,\u0026nbsp;朱子語類):\u003c/p\u003e\n\u003cp\u003e十目所視，十手所指」，言「小人閒居為不善」，其不善形於外者不可揜如此。「德潤身，心廣體胖\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOccidental Mnemonics\u003c/em\u003e \u003cem\u003e(Xiguo jifa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e西國紀法):\u003c/p\u003e\n\u003cp\u003e十目所視，十手所指，其嚴乎？富潤屋，德潤身，心廣體胖\u003c/p\u003e\n\u003cp\u003eThe above output is from model 2. The model can still recognize the text reuse even if the words are not the same or similar in the middle of the sentence. Such findings showcase the strength of the model in identifying textual connections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Advantages and Disadvantages of the Three Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 1 performs very well on smaller datasets but has limitations when it comes to comparing multiple texts simultaneously, as it can overload and eventually crash the website. Therefore, comparing the entire CText corpus can be time-consuming. It can detect a reasonable number of text reuses, and its main advantage is its convenience and user-friendly nature. Users only need a CText account, and all the work can be done online without the need for additional software\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eFigure 1 provides a sample output graph. \u0026nbsp;Here we take \u003cem\u003eThe True Meaning of the Lord of Heaven (Tianzhu shiyi\u003c/em\u003e, \u003cem\u003e天主實義\u003c/em\u003e\u003cem\u003e)\u0026nbsp;\u003c/em\u003eand its eight sub-chapters as an example to show the results of Model 1; green ovals mark different sub-chapters, and blue ovals represent reused ancient Chinese documents. The two have different thicknesses. are connected by blue lines, where the thicker the blue line, the more references it has. It is worth noting that the output does not include aligned matched quotes. Instead, it contains a similarity matrix, in which the subtitles in row names and column names use blue as the background color, and reused ancient documents use green as the background color. (Figure 2). While this format may not provide immediate insight into the state of reuse, it can still be valuable for further analysis and investigation.\u003c/p\u003e\n\u003cp\u003eUnfortunately, Model 2 did not perform well in our project. The main issue is the insufficient number of outputs even from a large corpus which is not enough for analysis. Considering the performance of the demo data, it appears that this problem may arise from an incompatible data format. Mistakes during the conversion of the TEI format or limitations in the conversion of Kanripo\u0026rsquo;s data to other formats might also be contributing factors.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e[1]\u003c/a\u003e However, it is worth noting that this model offers the best in-built visualization capabilities among the three models. It provides a website showcasing all aligned matched quotes with a well-designed interface. We take the citation of the \u003cem\u003eBook of Documents\u003c/em\u003e (\u003cem\u003eShang shu,\u003c/em\u003e 尚書) in the context of \u003cem\u003eThe True Meaning of the Lord of Heaven\u0026nbsp;\u003c/em\u003eas an example. In the output, the left side represents the entire paragraph from the classical text being cited, while the right side presents the excerpt from Matteo Ricci\u0026rsquo;s work that references the ancient literature. The matching portions between the two will be highlighted in red, indicating instances of reuse(Figure 3). Moreover, it offers additional features, such as the ability to explore passages by frequency of reuse and view passages on a timeline (Figure 4). Unfortunately, due to the absence of publication years in the Kanripo corpus, we are unable to utilize the timeline function. Nonetheless, the output graph (Figure 5) is clear and user-friendly, allowing for easy interpretation and control. As illustrated in Figure 5, the network visualization provides a clear and user-friendly representation of the shared instances of reused passages across multiple cited texts. The visualization effectively demonstrates that each of Ricci\u0026rsquo;s three distinct writings generates its own set of associated textual elements and reused spherical \u0026ldquo;constellations.\u0026rdquo; These constellations encompass recycled text sourced from various locations within the corpus. The visualization is easily interpretable and allows for convenient exploration and manipulation by users.\u003c/p\u003e\n\u003cp\u003eFrom a historical and philological perspective, all the models share several common limitations that are shared by many text-reuse detection methods. When two texts reuse the same sentence from a third text, there is a risk of misidentifying it as a reuse between texts one and two. In other words, the models cannot determine which texts serve as the source of the reused sentence. Additionally, as a language literary Sinitic is particularly rich in short phrases and idioms. For instance, when using a 4-gram analysis, frequently used expressions like\u0026nbsp;天地萬物\u0026nbsp;(\u003cem\u003etian di wan wu\u003c/em\u003e), meaning \u0026ldquo;everything on earth,\u0026rdquo; may be detected. These must be borne in mind when considering text reuse in Matteo Ricci\u0026rsquo;s oeuvre.\u003c/p\u003e\n\u003cp\u003eModel 3 performed the best in our analysis. It provides over a thousand matching quotes, all of which appear to be valid (Figures 6 \u0026amp; 7). The instructions for this model are clear and easy to follow. Additionally, the model provides a text file containing all the matched quotes (Figure 8), including the analyzed works of Matteo Ricci, the cited ancient Chinese documents, the length of repetitive sequences involved in the analysis, and the recorded reuse sequences location and other information. In this case, models 1 and 2 have a better visualization.\u003c/p\u003e\n\u003cp\u003eOverall, our goal is to identify the text reuse patterns in Ricci\u0026rsquo;s work, and the data itself is more important to us than the visualization. Therefore, Model 3 is the preferred choice for our project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Other Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNowadays, there are numerous models available for detecting text reuse, e.g. GanymedeNil/text2vec-large-chinese (https://huggingface.co/GanymedeNil/text2vec-large-chinese). These models generally perform well in finding similar sentences between two texts rather than between two corpora. However, it\u0026rsquo;s important to note that in a digital humanities context comparing texts using these models can be time-consuming, as they are not designed with our particular needs in mind.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModels 1, 2, and 3, which we have discussed earlier, have been well-developed and utilize their algorithms for text comparison. They incorporate various techniques to reduce the time required for finding text reuses in large corpora. These techniques include data preprocessing, sampling, chunking, and the utilization of stop-word lists. Without these techniques, it would be impractical to use general models available on the internet, as they would take a significant amount of time to output results. Meanwhile, it can be challenging for researchers to develop their code to address this issue. Therefore, it is more efficient and convenient to utilize the developed models that come with complete instructions and guidelines.\u003c/p\u003e\n\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e[1] Unfortunately, the instructions and code provided for this model also contain several errors.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Analysis","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Introduction\u003c/h2\u003e\n \u003cp\u003eThis study aims to examine the attributes of the referenced texts within Matteo Ricci\u0026rsquo;s writings, encompassing preferences for citation and variations in reuse practices across different texts. Visualizations are employed to facilitate the analysis of these characteristics. Furthermore, we seek to explore the association between the frequency of citations of classical and other texts in Literary Sinitic and their specific attributes, such as the historical dynasty of origin, school of thought, and genre. This analysis is conducted through correlation analysis. Additionally, a diverse range of regression models, including multiple linear regression and regression trees, are utilized to identify the features that exert a more pronounced influence on citation frequency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Data Overview\u003c/h2\u003e\n \u003cp\u003eThe dataset used in this study is derived from similarity comparison data generated by employing the CText text tool plugin and its associated corpus. Following the manual identification and selection of citation entries to enhance reliability and accuracy, missing values were addressed using multiple imputation techniques. Multiple imputation involves estimating missing values based on the observed data, creating multiple plausible imputed datasets. Additionally, entries containing missing data were also removed from the dataset. As a result, the dataset encompasses 45 Chinese texts that were referenced in the works of Matteo Ricci, obtained from the CText corpus. Each text is accompanied by its corresponding features, such as the year of composition, themes, philosophical schools, and other pertinent information. To meet the requirements of subsequent analyses, certain categorical variables were transformed into dummy variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Analysis and Results\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.1 Approach for Analysis\u003c/h2\u003e\n \u003cp\u003eWe first extracted relevant data from the whole dataset according to different analysis requirements. Because the exact genesis of some texts is obscure, some characteristics are missing. Therefore, each time after selection, we first removed the rows which contain missing values. After the data cleaning, we did the classifying and summation to obtain the total number of instances of text reuse about certain classification. Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e are the data processing results, including total numbers of text reuse in different dynasties and the different schools of thoughts. Moreover, we then counted the reuse times in a single work by Ricci for comparison. We only chose those works which had high numbers of citations. The reason is that if the total number is too small, a small value can still represent a significant percentage. Therefore, the percentage computation may not have a typical meaning and may cause some special value impacts.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDynasty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal citation times\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in\u003c/p\u003e\n \u003cp\u003e辯學遺牘\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in\u003c/p\u003e\n \u003cp\u003e天主實義\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in Other Texts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWarring States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpring and Autumn period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthern Song\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Zhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWei and Jin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Song\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthern and Southern Dynasty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSchools of Thought\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal citation times\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in 天主實義\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in 辯學遺牘\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitation times in Other Texts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfucianism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaoism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEclectics Ideology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLegalism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMohism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.2 Data Visualization\u003c/h2\u003e\n \u003cp\u003eThe texts reused range from the Western Han Dynasty (\u003cem\u003ec\u003c/em\u003e. 1100-771 BCE) to the Ming Dynasty (1368\u0026ndash;1644 CE) and all the main schools of thought are represented. In Figs. 9 to \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e, we can see that in terms of chronology, the Spring and Autumn period and Warring States were the most highly cited across Ricci\u0026rsquo;s works. In terms of schools of thought, the texts classed as Confucianism in the metadata are dominant with Taoism also well represented. From longitudinal comparison, we found that there were no significant differences in text reuse across Ricci\u0026rsquo;s publications.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Analysis of the Characteristics of Cited Works\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.1 Approach to Analysis\u003c/h2\u003e\n \u003cp\u003eWe primarily analyze the inherent features of the referenced ancient Chinese works to determine the presence of certain preferences in Matteo Ricci\u0026rsquo;s writings. We focus on the following two aspects: the examination and analysis of the correlation between the features of the referenced works and the writings, and the construction of multiple regression models to identify the most influential features on citation counts.\u003c/p\u003e\n \u003cp\u003eRegarding the examination of correlation, correlation coefficients are primarily used as the main criterion. To analyze feature importance, we employed two common approaches: linear regression and nonlinear regression (regression trees). Categorical variables including features of ancient works were transformed into dummy variables to meet the needs of regression model construction.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.2 Visualization and Statistical Discussion of Results\u003c/h2\u003e\n \u003cp\u003eWe conducted a correlation analysis on different works by Matteo Ricci and the features of the cited works, including the year of publication, work genre, and philosophical school.\u003c/p\u003e\n \u003cp\u003eIn this correlation study, we used the Pearson correlation coefficient. In simple terms, this is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. It ranges from \u0026minus;\u0026thinsp;1 to 1, where a value of 1 indicates a perfect positive linear correlation, -1 indicates a perfect negative linear correlation, and 0 indicates no linear correlation.\u003c/p\u003e\n \u003cp\u003eBased on the correlation coefficient matrix, we plotted heatmaps as shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e to visually represent the relationships between variables. The heatmaps provide a color-coded representation of the correlation values, where darker shades indicate stronger correlations and lighter shades indicate weaker correlations.\u003c/p\u003e\n \u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e, we visualized the correlations between the works of Matteo Ricci and features of other texts in Literary Sinitic. When the Pearson coefficient between different variables is greater than 0.5, it can be considered as having a certain degree of linear correlation. For example, we can infer a significant correlation between Ricci\u0026rsquo;s \u003cem\u003eTen Discourses on the Man of Paradox\u003c/em\u003e (\u003cem\u003eJiren shi pian\u003c/em\u003e, \u003cem\u003e畸人十篇)\u003c/em\u003e and texts that fall into the genre of poetry.\u003c/p\u003e\n \u003cp\u003eHowever, in Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e, by analyzing the correlations among various features of Chinese literature, we also observed that there are linear correlations among the features of cited works. These features can influence the accuracy of subsequent linear regression models, and will be treated momentarily.\u003c/p\u003e\n \u003cp\u003eWe also constructed Ordinary Least Squares (OLS), Lasso, Ridge Regression, and Support Vector Regression (SVR) models using the characteristics of the reused texts and their citation counts, and analyzed and compared these models to determine which features had a greater impact on the citation situation.\u003c/p\u003e\n \u003cp\u003eTo do so, we split the original dataset into a training set and a testing set with a ratio of 1:4. The training set was used to build the regression models, and the predicted values were compared with the actual values in the testing set. The performance of different linear regression models (predicted values vs. actual values) is shown in Fig. \u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eRoot Mean Squared Error (RMSE) and Mean Absolute Error (MAE) of the training and testing sets were used as evaluation metrics to assess the performance of the regression models. RMSE is a commonly used metric that measures the average magnitude of the residuals or prediction errors, taking into account both the magnitude and direction of the errors. It is calculated by taking the square root of the average of the squared differences between the predicted values and the corresponding true values. RMSE provides a measure of the overall goodness of fit of the model, with lower values indicating better performance.\u003c/p\u003e\n \u003cp\u003eOn the other hand, Mean Absolute Error (MAE) is another evaluation metric that measures the average magnitude of the absolute differences between the predicted values and the true values. Unlike RMSE, MAE does not consider the squared differences and only focuses on the absolute differences. MAE provides a measure of the average magnitude of the errors, without considering their direction. Similar to RMSE, lower values of MAE indicate better performance of the regression model (Tianfeng, 2022).\u003c/p\u003e\n \u003cp\u003eThe relevant results of the RMSE and MAE for the training and testing sets are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e below. These metrics help us understand how well the regression models were able to predict the target variable and compare the performance of different models.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrain RMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest RMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrain MAE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest MAE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLasso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRidge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.4933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIt can be observed that OLS and Lasso perform the best. However, it is well-known that there exists a significant issue of multicollinearity among the various features of the cited works, which inevitably adversely affects the accuracy of our linear regression model. To assess the reliability of the linear regression model, significance testing (including F-test and t-test) was employed as the basis for evaluation.\u003c/p\u003e\n \u003cp\u003eSignificance testing is a statistical method used to determine the significance of relationships or differences in data. In the context of linear regression models, significance testing helps assess the statistical significance of the model and its coefficients. In this case, the significance tests reveal that the constructed linear regression model, which incorporates information such as the number of citations of classical Chinese texts, the dynasty of composition, genre, and philosophical school, is not accurate. This lack of accuracy may be attributed to the presence of severe multicollinearity among the features of the cited works.\u003c/p\u003e\n \u003cp\u003eUsing the most effective Ordinary Least Squares (OLS) model as an example, according to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, we observed that both the results of the overall significance test (f-test) for the entire model and the significance test of individual coefficients (t-test) indicate that the linear regression model built upon information such as the number of citations of ancient Chinese texts, the dynasty of composition, genre, and philosophy school is not sufficiently precise.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOLS Regression Results\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDep. Variable:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj. R-squared:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethod:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeast Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. Observations:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb (F-statistic):\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf Residuals:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog-Likelihood:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-72.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf Model:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCovariance Type:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNonrobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the results of correlation and significance tests, we discovered the presence of significant multicollinearity in the linear regression models using the characteristics of ancient works and their citation counts. To further validate the previous findings and address the issue of multicollinearity, we incorporated regression tree analysis into our methodology. Regression tree analysis is a decision tree-based approach that allows for the identification of important features.\u003c/p\u003e\n \u003cp\u003eIn our study, we employed various regression tree techniques, including random forest, Tree with AdaBoost, and Tree with Gradient boosting, to conduct an analysis of the significant features. These techniques aim to capture complex nonlinear relationships and interactions among the variables.\u003c/p\u003e\n \u003cp\u003eFor instance, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e19\u003c/span\u003e illustrates the utilization of the regression tree principle. We employed the standard deviation as the criterion for branching in the regression tree. The splitting point was determined based on the calculation of the standard deviation of the target variable, which represents the predicted value, for each branch.\u003c/p\u003e\n \u003cp\u003eThe results of the analysis of important features using four regression tree models are shown in Figs. \u003cspan class=\"InternalRef\"\u003e20\u003c/span\u003e to \u003cspan class=\"InternalRef\"\u003e23\u003c/span\u003e. Overall, in terms of citation count, the \u0026ldquo;Warring States\u0026rdquo; period appears to be the greatest driver. In terms of philosophical schools, the trend known as \u0026ldquo;Confucianism\u0026rdquo; appears to be the primary influencing factor. In terms of genre, both \u0026ldquo;Poetry\u0026rdquo; and \u0026ldquo;Philosophy\u0026rdquo; are prominent.\u003c/p\u003e\n \u003cp\u003eIt is worth mentioning that in terms of chronology of the reused texts, the importance of the \u0026ldquo;Spring and Autumn Period\u0026rdquo; as an influencing factor is not significantly different from the \u0026ldquo;Warring States\u0026rdquo; period. This may be because both periods belong to the \u0026ldquo;Pre-Qin Period,\u0026rdquo; a crucial period for the development of philosophy, including the rise of various philosophical schools such as Confucianism, Taoism, and Mohism, all of which continued to play important roles in Ricci\u0026rsquo;s day.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.3 Explanation and Limitations of Our Analysis\u003c/h2\u003e\n \u003cp\u003eBased on the analysis of linear and non-linear regression models presented earlier, we can conclude that Matteo Ricci\u0026rsquo;s writings exhibit a preference for citing and reworking texts with specific characteristics. To wit, there is a higher tendency to cite works from the Warring States period, works related to Confucianism, and poetic works. This preference can be understood as Matteo Ricci\u0026rsquo;s search for strong and familiar foundations on which to build his presentation of Christian teachings. As is well known, Ricci\u0026rsquo;s aligned the Christian God \u0026ldquo;the Lord of Heaven\u0026rdquo; (\u003cem\u003eTianzhu 天主\u003c/em\u003e), with a diety frequently mentioned in classical texts called \u0026ldquo;the Lord Above\u0026rdquo; (\u003cem\u003eshangdi 上帝)\u003c/em\u003e, as expressed in his mantra \u0026ldquo;The Lord of Heaven and the Lord Above agree in doctrine\u0026rdquo; (\u003cem\u003eTianzhu Shangdi Tongyilun\u003c/em\u003e, \u003cem\u003e天主上帝同一論)\u003c/em\u003e (Goodman and Grafton \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ji \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Ricci faced challenges in his missionary work in China primarily due to the deep-rooted reverence for their own traditions among the ruling class and elites, which often resulted in skepticism and even rejection of Christianity as a destructive influence (Fontana, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). To achieve his missionary goals, Ricci integrated Christian theories with Confucianism, which held a prominent position in Chinese intellectual circles. He interpreted the God mentioned in ancient texts as the Christian God, appealing to classics such as the \u003cem\u003eShi Jing (詩經)\u003c/em\u003e and the \u003cem\u003eShu Jing (書經)\u003c/em\u003e from the Spring and Autumn and Warring States periods (Ji \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). By doing so, he sought to mitigate the ruling class\u0026rsquo;s rejection of Christian theories within the context of cultural recognition and reverence for ancient traditions, ultimately facilitating the acceptance of Christianity in China (Goodman and Grafton, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOf course, there are limitations to this study, some inherent in its limited scope, others in Ricci\u0026rsquo;s writings themselves. There is significant variation in the length of Matteo Ricci\u0026rsquo;s writings, leading to differences in the number of reuses among different works. Some texts may not contain any citations, indirectly affecting the analysis of citation preferences. Different larger corpora of texts in Literary Sinitic might also yield different results due to a range of factors, such as completeness, quality of text pre-processing, etc.\u003c/p\u003e\n \u003cp\u003eWe therefore encourage digital sinologists to redo this analysis focusing on an author who has a larger or more varied oeuvre. Ricci is a towering figure in the history of China-Western relations, and so his works are as good a starting point as any. However, it should be repeated across a range of authors for a richer sense of how the 3 models perform and the sort of insights can be gleaned from them. Similarly, different corpora can be used.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.4 Supplementary Analysis of Reuse Practice Using a Larger Corpus\u003c/h2\u003e\n \u003cp\u003eTo mitigate some of the latter limitations of this study, a supplementary analysis was conducted to further investigate the reuse practice in Matteo Ricci\u0026rsquo;s works by utilizing a larger corpus, specifically Kanripo. This analysis aims to provide additional insights to complement the experimental results obtained from CText and CText text tool.\u003c/p\u003e\n \u003cp\u003eDue to the large number of results, manual labeling and statistical analysis of all the data proved challenging. However, with the help of Kanripo\u0026rsquo;s categorization, we were able to analyze the data using visual representations. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e24\u003c/span\u003e displays a total of 1,092 results, with 876 stemming from \u003cem\u003eThe True Meaning of the Lord of Heaven (天主實義)\u003c/em\u003e, 59 from \u003cem\u003eXiguo Jifa (西國紀法)\u003c/em\u003e, 3 from the \u003cem\u003eTranslation of Euclid\u0026rsquo;s Elements (譯幾何原本引)\u003c/em\u003e, 138 from \u003cem\u003eChong Ke Ji Ren Shi Pian (重刻畸人十篇)\u003c/em\u003e, 4 from \u003cem\u003eOn Friendship (交友論)\u003c/em\u003e, 12 from \u003cem\u003eBian Xue Yi Du (辨學遺牘)\u003c/em\u003e, and none from both \u003cem\u003eXiji Qiji (西字奇蹟) and Er Shi Wu Yan (二十五言)\u003c/em\u003e. These numbers align with our expectations based on the lengths of the respective texts.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e25\u003c/span\u003e then illustrates the distribution by genre of the total results. 665 are from the Classics, 137 from History, 144 from Philosophy, 103 from Miscellaneous Works, 6 from Taoist works, and 28 from Buddhist works. In Fig. \u003cspan class=\"InternalRef\"\u003e26\u003c/span\u003e, the genres are further divided into smaller categories, and it is evident that the Documentary (書類) subcategory contributes significantly to the reuses. One notable example within this category is \u003cem\u003eVenerated Documents (Shang shu, 尚書)\u003c/em\u003e, also known as \u003cem\u003eThe Book of Documents (Shu jing\u003c/em\u003e, \u003cem\u003e書經)\u003c/em\u003e, which contains a variety of orations and dialogues by rulers and officials. According to the result, there are 249 instances of text reuse found in \u003cem\u003eShang Shu\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eFor instance, in \u003cem\u003eThe True Meaning of the Lord of Heaven\u003c/em\u003e Ricci reused the following two passages from the \u003cem\u003eShang Shu\u003c/em\u003e:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. \u0026ldquo;The King Wen of Zhou who is respectful, humble, diligent and striving to serve that God,\u0026rdquo; (維此文王, 小心翼翼, 昭事上帝).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e2. \u0026ldquo;The Lord God endowed all people with a benevolent nature. However, the one who can truly guide their lives by their inherent goodness and bring stability is none other than the king,\u0026rdquo; (惟皇上帝降衷于下民, 若有恆性, 克綏厥猷惟后).\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eHere, we see Ricci making efforts to establish a connection between the ancient deity and the God of Catholicism that he presented as one and the same.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eTo advance the Jesuit missionary enterprise, Matteo Ricci employed a combination of Christian teachings, Greco-Roman philosophy and authoritative classical Chinese texts in his works. By emphasizing the compatibility between Confucianism and Christianity, Ricci garnered admiration from Ming elites and became a source of inspiration for intellectually curious scholars. However, previous studies have not adopted digital models to analyze Ricci\u0026rsquo;s test reuse patterns of Chinese works. Hence, this research aimed to fill the gap by digitizing, uploading, and analyzing Matteo Ricci\u0026rsquo;s compositions in literary Sinitic within the wider context of the Sinographic literary tradition.\u003c/p\u003e \u003cp\u003eUltimately, our analysis chimes with the existing scholarship in showing a preference for citing ancient compositions from the Warring States period, those related to Confucianism, and those with poetic or philosophical topics. In terms of methodology, all three models that we used had their limitations. In the end, two texts reusing the same sentence from another text will be wrongly identified as reuse between them. They have no way to recognize which texts are the source of reuse. For the purposes of this analysis, Model 3 emerged as the most useful. It can generate more results and provides a concrete analysis of Ricci\u0026rsquo;s reuse. We suggest that digital sinologists use this as a starting point in their own work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research was undertaken as a summer project by student members of the Digital History Lab overseen by Prof. Stuart M. McManus who provided the idea for the project. Leo Tam did much of the preliminary analysis and assessment of the 3 models. Yuji Li and Shuyang Qiu worked primarily on the statistical analysis. Songyu Liu and Letian Yu worked on the visualizations and related tasks. As history students, Daniel Ng and Warner Wong assisted with the historical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHarbsmeier, C. (2018). \u0026ldquo;Matteo Ricci on Friendship: Some Latin Sources for his Chinese Book.\u0026rdquo; In Iwo Amelung, Joachim Kurtz (eds.) In \u003cem\u003eReading the Signs: Philology, History, Prognostication Festschrift for Michael Lackner.\u003c/em\u003e M\u0026uuml;nchen: Iudicium Verlag, 2018.\u003c/li\u003e\n\u003cli\u003eHosne, A. C. (2014). Friendship among Literati. Matteo Ricci SJ (1552\u0026ndash;1610) in Late Ming China. The Journal of Transcultural Studies, 5(1), 190\u0026ndash;214. https://doi.org/10.11588/ts.2014.1.11362\u003c/li\u003e\n\u003cli\u003eSpalatin, C. (1975). Matteo Ricci\u0026rsquo;s Use of Epictetus\u0026rsquo; Encheiridion. \u003cem\u003eGregorianum\u003c/em\u003e, 56(3), 551-557\u003c/li\u003e\n\u003cli\u003eSpence, J. D. (1984). \u003cem\u003eThe Memory Palace of Matteo Ricci\u003c/em\u003e. London: Faber and Faber.\u003c/li\u003e\n\u003cli\u003eSturgeon, D. (2018). Digital Approaches to Text Reuse in the Early Chinese Corpus. Journal of Chinese Literature and Culture, 5(2), 186-213. https://doi.org/10.1215/23290048-7256963\u003c/li\u003e\n\u003cli\u003eMungello, D. E. (1985). \u003cem\u003eCurious Land: Jesuit Accommodation and the Origins of Sinology\u003c/em\u003e. Stuttgart: F. Steiner Verlag Wiesbaden.\u003c/li\u003e\n\u003cli\u003eJi, J. (2014). The origin of Sino-Christian Theology: Matteo Ricci\u0026rsquo;s idea of Shang-ti, T\u0026rsquo;ien and Translation of \u0026lsquo;Deus\u0026rsquo; in Late-Ming China. \u003cem\u003eSino-Christian Studies\u003c/em\u003e, 17(2014): 47-74.\u003c/li\u003e\n\u003cli\u003eTharsen, J. and Gladstone, C. (2022). \u0026ldquo;TextPAIR Viewer (TPV) 1.0: An Interactive Visual Toolkit for Exploring Networks of Textual Alignments and Text Reuse.\u0026rdquo; \u003cem\u003eShuzi renwen\u003c/em\u003e 數字人文 2: https://www.dhcn.cn/site/works/dhjournal/20452.html\u003c/li\u003e\n\u003cli\u003eGoodman, H. L., and Grafton A. (1990). Ricci, the Chinese, and the Toolkits of Textualists. \u003cem\u003eAsia Major\u003c/em\u003e, 2, 95-148.\u003c/li\u003e\n\u003cli\u003eFerrero, M. (2019). \u0026ldquo;Motivation to Act in Confucianism and Christianity: In Matteo Ricci\u0026rsquo;s The True Meaning of the Lord of Heaven (Tianzhu Shiyi 天主實義).\u0026rdquo; \u003cem\u003eFrontiers of philosophy in China\u003c/em\u003e 14, no. 2 (2019): 226\u0026ndash;247. \u003c/li\u003e\n\u003cli\u003eFontana, M. (2011). \u003cem\u003eMatteo Ricci: A Jesuit in the Ming Court\u003c/em\u003e. Lanham, MD: Rowman \u0026amp; Littlefield Publishers.\u003c/li\u003e\n\u003cli\u003eMcManus, S. M. (2021). \u003cem\u003eEmpire of Eloquence: The Classical Rhetorical Tradition in Colonial Latin America and the Iberian World\u003c/em\u003e. Cambridge: Cambridge University Press. \u003c/li\u003e\n\u003cli\u003eRicci, M. (1985). \u003cem\u003eThe True Meaning of the Lord of Heaven: T\u0026rsquo;ien-Chu Shih-I Chinese-English\u003c/em\u003e ed. Douglas Lancashire, Peter Hu Kuo-chen, and Edward Malatesta. Taipei: Institut Ricci.\u003c/li\u003e\n\u003cli\u003eStandaert, N. 1999. \u0026ldquo;The Jesuits Did NOT Manufacture \u0026lsquo;Confucianism.\u0026rsquo;\u0026rdquo; \u003cem\u003eEast Asian Science, Technology, and Medicine\u003c/em\u003e, 16, pp. 115\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eHsia, R. P. (2016) \u003cem\u003eMatteo Ricci and the Catholic Mission to China, 1583-1610: A Short History with Documents.\u003c/em\u003e Indianapolis: Hackett Publishing Company.\u003c/li\u003e\n\u003cli\u003eVierthaler, P. and Gelein, M. (2019). \u0026ldquo;A BLAST-Based, Language-Agnostic Text Reuse Algorithm with a MARKUS Implementation and Sequence Alignment Optimized for Large Chinese Corpora.\u0026rdquo; \u003cem\u003eJournal of Cultural Analytics\u003c/em\u003e, 4.2. https://doi.org/10.22148/16.034.\u003c/li\u003e\n\u003cli\u003eChai, T. (2022). Root Mean Square. In: Daya Sagar, B.S., Cheng, Q., McKinley, J., Agterberg, F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_280-1\u003c/li\u003e\n\u003cli\u003eWitek, J. W. (2015). \u0026ldquo;Christianity and China: Universal Teaching from the West,\u0026rdquo; In Uhalley, S,, and Wu, X. (eds) \u003cem\u003eChina and Christianity: Burdened Past, Hopeful Future\u003c/em\u003e. London: Routledge.\u003c/li\u003e\n\u003cli\u003eLiu, Y. (2015). \u003cem\u003eHarmonious Disagreement: Matteo Ricci and His Closest Chinese Friends\u003c/em\u003e. New York: Peter Lang.\u003c/li\u003e\n\u003cli\u003eZou, Z. 邹振环 (2001). \u0026ldquo;Limadou \u0026lsquo;Jiaoyoulun\u0026rsquo; de yikan yu chuanbo\u003c/li\u003e\n\u003cli\u003e利玛窦《交友论》的译刊与传播 (Translation and Publication of Matteo Ricci\u0026rsquo;s \u003cem\u003eOn Friendship\u003c/em\u003e).\u0026rdquo; \u003cem\u003eFudan Journal (Social Sciences)\u003c/em\u003e 3: 49-55.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Unfortunately, the instructions and code provided for this model also contain several errors.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-digital-humanities","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijdh","sideBox":"Learn more about [International Journal of Digital Humanities](http://link.springer.com/journal/42803)","snPcode":"42803","submissionUrl":"https://submission.nature.com/new-submission/42803/3","title":"International Journal of Digital Humanities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4471619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4471619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article analyses the debt to classical Sinographic literature, history and philosophy in the works of the famous Jesuit missionary to the Wanli Emperor, Matteo Ricci (1552-1610). To do so, we trial three recently developed models for analyzing historical text reuse and choose the model best suited to our task. After identifying relevant examples of text reuse in Ricci’s eight surviving works (two of which show particularly high patterns of reuse), we then explore the possible historical factors behind these patterns and subject the citation frequencies of particular classical works, historical periods and genres to analysis using linear and nonlinear regression models. In the end, we conclude that Ricci’s oeuvre borrows heavily from works generally categorized as “Confucian” from the Warring States period, as well as poetic texts of various sorts. This reflects the existing consensus in the historiography, while adding important granular detail.\u003c/p\u003e","manuscriptTitle":"Evaluating Existing Approaches to Text Reuse Through an Analysis of Matteo Ricci’s Reliance on Classical Chinese Texts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 12:20:27","doi":"10.21203/rs.3.rs-4471619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-29T20:18:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-08T09:26:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-27T05:31:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Digital Humanities","date":"2024-05-24T09:46:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-digital-humanities","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijdh","sideBox":"Learn more about [International Journal of Digital Humanities](http://link.springer.com/journal/42803)","snPcode":"42803","submissionUrl":"https://submission.nature.com/new-submission/42803/3","title":"International Journal of Digital Humanities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"47720d57-b15a-4bc6-ae32-8f2a693a1852","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:11:26+00:00","versionOfRecord":{"articleIdentity":"rs-4471619","link":"https://doi.org/10.1007/s42803-025-00104-w","journal":{"identity":"international-journal-of-digital-humanities","isVorOnly":false,"title":"International Journal of Digital Humanities"},"publishedOn":"2025-06-30 15:57:53","publishedOnDateReadable":"June 30th, 2025"},"versionCreatedAt":"2024-06-11 12:20:27","video":"","vorDoi":"10.1007/s42803-025-00104-w","vorDoiUrl":"https://doi.org/10.1007/s42803-025-00104-w","workflowStages":[]},"version":"v1","identity":"rs-4471619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4471619","identity":"rs-4471619","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}