[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120321-en":3,"doc-seo-120321-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120321,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Predicting retracted research - a dataset and machine learning approaches","Retractions weaken the reliability of the scientific record and can cause flawed findings to persist. This study builds an open-access dataset combining retraction information with bibliographic metadata, then trains and evaluates multiple machine learning approaches to predict retracted articles. Ablation studies quantify each feature’s contribution. Results compare accuracy, precision, recall, and F1-score, identifying strong model performance and motivating automated support for publishers and reviewers.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of Sheffield.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/226914/](https://eprints.whiterose.ac.uk/id/eprint/226914/)  \nVersion: Published Version  \nArticle:  \nFletcher, A.H.A. and Stevenson, M. (2025) Predicting retracted research: a dataset and machine learning approaches. Research Integrity and Peer Review, 10. 9. ISSN: 2058- 8615  \n[https://doi.org/10.1186/s41073-025-00168-w](https://doi.org/10.1186/s41073-025-00168-w)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nFletcher and Stevenson Research Integrity and  \nResearch Integrity and Peer Review (2025) 10:9  \n[https://doi.org/10.1186/s41073-025-00168-w](https://doi.org/10.1186/s41073-025-00168-w Peer Review)[ Peer Review](https://doi.org/10.1186/s41073-025-00168-w Peer Review)  \n RESEARCH Open Access  \nPredicting retracted research: a dataset and machine learning approaches  \nAaron H. A. Fletcher 1* and Mark Stevenson 1  \nAbstract  \nBackground Retractions undermine the scientific record’s reliability and can lead to the continued propagation of flawed research. This study aimed to (1) create a dataset aggregating retraction information with bibliographic metadata,(2) train and evaluate various machine learning approaches to predict article retractions, and (3) assess each feature’s contribution to feature-based classifier performance using ablation studies.  \nMethods An open-access dataset was developed by combining information from the Retraction Watch database and the OpenAlex API. Using a case-controlled design, retracted research articles were paired with non-retracted articles published in the same period. Traditional feature-based classifiers and models leveraging contextual language representations were then trained and evaluated. Model performance was assessed using accuracy, precision, recall, and the F1-score.  \nResults The Llama 3.2 base model achieved the highest overall accuracy. The Random Forest classifier achieved a precision of 0.687 for identifying non-retracted articles, while the Llama 3.2 base model reached a precision of 0.683 for identifying retracted articles. Traditional feature-based classifiers generally outperformed most contextual language models, except for the Llama 3.2 base model, which showed competitive performance across several metrics. Conclusions Although no single model excelled across all metrics, our findings indicate that machine learning techniques can effectively support the identification of retracted research. These results provide a foundation for developing automated tools to assist publishers and reviewers in detecting potentially problematic publications. Further research should focus on refining these models and investigating additional features to improve predictive performance.  \nTrial registration Not applicable.  \nKeywords Retraction prediction, Machine learning, Scientific publishing  \nBackground  \nRetracting scientific articles is essential for safeguarding the integrity of the research record, but the growing number of retractions also reveals weaknesses in peer review and editorial oversight [1, 2]. Determining the extent of retractions is complicated by “st","cbCaifwkT8zEqAC8","https://ap.wps.com/l/cbCaifwkT8zEqAC8","pdf",505785,1,11,"English","en",105,"# Background\n## Stealth retractions and editorial challenges\n# Methods\n## Dataset construction\n## Training and evaluation\n# Results\n## Model performance and feature comparison\n# Conclusions\n## Automated detection and future work","[{\"question\":\"What was the main goal of the study on predicting retracted research?\",\"answer\":\"To create a dataset with bibliographic metadata, train and evaluate machine learning models for retraction prediction, and measure the contribution of each feature using ablation studies.\"},{\"question\":\"How was the dataset constructed in this research?\",\"answer\":\"It combined information from the Retraction Watch database with the OpenAlex API, using a case-controlled design pairing retracted and non-retracted articles published in the same period.\"},{\"question\":\"Which models and metrics were used to evaluate performance?\",\"answer\":\"Traditional feature-based classifiers and contextual language representation models were trained and assessed using accuracy, precision, recall, and the F1-score.\"}]","Predicting retracted research - 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