Restoring the Credibility of AIGC Rate: An AIGC Rate Calibration Framework Based on Metacognitive Contribution (MS) and Machine Evidence (MR)

Restoring the Credibility of AIGC Rate: An AIGC Rate Calibration Framework Based on Metacognitive Contribution (MS) and Machine Evidence (MR) | 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 Article Restoring the Credibility of AIGC Rate: An AIGC Rate Calibration Framework Based on Metacognitive Contribution (MS) and Machine Evidence (MR) XIN LIN, Haiqun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7638610/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current evaluation dimensions of AIGC detection overly focus on the "suspected AI text rate" while neglecting the contribution differences between humans and AI in effective output. Such tools rely on modeling text statistical features, making them unable to counter adversarial tactics like semantic paraphrasing and cross-linguistic translation—exposing fundamental flaws in the single-feature detection paradigm.This study introduces a metacognitive perspective and proposes the "MS×MR" calibration framework, which takes cognitive contribution (MS) and machine detection evidence (MR) as dual evidence. Through conflict direction- and intensity-aware fusion, the framework outputs trustworthy and auditable suspected AIGC rates.Empirical results show that:On cross-linguistic datasets (M=10, equal-width binning), the Brier score of MR-only decreased from 0.003323 to 0.000459, ECE from 0.03606 to 0.00963, and MCE from 0.0893 to 0.03303.Conclusions remained robust across different binning schemes (M=10/20/50; equal-width/equal-frequency), with improvements in ΔBS/ BSS being significant under paired permutation tests (p<0.001) ； Global 1000×1000 grid analysis reveals that:The proportion of NEG was approximately 56%; the proportions of strong/medium/weak intensity were roughly 29.9%/30.4%/39.7%.The HUMAN_REVIEW (HR) trigger rate was about 10.27%, which was highly consistent with the POS×STRONG combination, forming a quantifiable review budget boundary ； In user interviews (n=60):The persuasiveness of the report increased from 2.03±0.78 to 3.48±0.62.Approximately 98.3% of participants reported reduced outcome anxiety.For samples of "non-malpractic cross-linguistic polishing/translation," HR=0, and the framework balanced both calibration and resolution.Overall, through the dual calibration of "cognition × machine," MS×MR overcomes the key flaws of the single-feature paradigm, providing a trustworthy, robust, and interpretable AIGC rate detection solution for high-stakes scenarios such as academic integrity. Physical sciences/Mathematics and computing Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Metacognitive Contribution AIGC Detection Calibration Machine Evidence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7638610","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":556940089,"identity":"a76ed28f-b08f-4bf8-87e1-a5213268138f","order_by":0,"name":"XIN LIN","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"XIN","middleName":"","lastName":"LIN","suffix":""},{"id":556940092,"identity":"0ee9a5e1-952c-47f2-a22c-69415227a954","order_by":1,"name":"Haiqun Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFACHgaGBCDFz8x88AFpWiTb2ZINiNcCAgbnecwEiNKg23724IcHZYfzjA8zmDEw1NhEE9RidiYvWSLh3OFis8MMaQ8YjqXlNhDUciDHQCKx7XDitsMMxw0YGw4ToeX8G+MfIC2bmxnbJIjTciPHDGzLBmZmNmK1vDGzSDiXnjjjMBuzQQJRfjmfY3zzR5l1Yn//+Y8PPtTYENYCAWxQOoE45chaRsEoGAWjYBRgAwBkHEHQl1FC4wAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Haiqun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-09-17 09:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7638610/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7638610/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97779720,"identity":"adcf61e6-19d6-4bf7-94af-c2fcbd60788b","added_by":"auto","created_at":"2025-12-09 09:30:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":514309,"visible":true,"origin":"","legend":"","description":"","filename":"RestoringtheCredibilityofAIGCRate.docx","url":"https://assets-eu.researchsquare.com/files/rs-7638610/v1/8f48b1f6f8f9eb2bfad5d9c5.docx"},{"id":97779719,"identity":"8fb8eec8-eb5f-4d68-a449-0a13ba428b8a","added_by":"auto","created_at":"2025-12-09 09:30:29","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5101,"visible":true,"origin":"","legend":"","description":"","filename":"b1ab739794b34eb287abff8739ca2d20.json","url":"https://assets-eu.researchsquare.com/files/rs-7638610/v1/e5cc448381d1f6e4730d9a95.json"},{"id":101096097,"identity":"bfb4a99d-9d63-4b78-ba7a-7b9a6ba1b015","added_by":"auto","created_at":"2026-01-25 22:23:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":871603,"visible":true,"origin":"","legend":"","description":"","filename":"RestoringtheCredibilityofAIGCRate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7638610/v1_covered_7a0540eb-c9ac-459b-a5e7-16b5b91caf6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Restoring the Credibility of AIGC Rate: An AIGC Rate Calibration Framework Based on Metacognitive Contribution (MS) and Machine Evidence (MR)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metacognitive Contribution, AIGC Detection, Calibration, Machine Evidence","lastPublishedDoi":"10.21203/rs.3.rs-7638610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7638610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Current evaluation dimensions of AIGC detection overly focus on the \"suspected AI text rate\" while neglecting the contribution differences between humans and AI in effective output. Such tools rely on modeling text statistical features, making them unable to counter adversarial tactics like semantic paraphrasing and cross-linguistic translation—exposing fundamental flaws in the single-feature detection paradigm.This study introduces a metacognitive perspective and proposes the \"MS×MR\" calibration framework, which takes cognitive contribution (MS) and machine detection evidence (MR) as dual evidence. 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