Memon, Shahan Ali and West, Jevin and O'Connor, Cailin (2025) Are Widely Known Findings Easier to Retract? [Preprint]

Text Retraction_Models_and_Data, deanon, spring 2025.pdf Download (1MB)

Abstract

Failures of retraction are common in science. Why do these failures occur? And, relatedly, what makes findings harder or easier to retract? We use data from Microsoft Academic Graph, Retraction Watch, and Altmetric---including retracted papers, citation records, and Altmetric scores and mentions---to test recently proposed answers to these questions. LaCroix et al. (2021) employ simple network models to argue that the social spread of scientific information helps explain failures of retraction. One prediction of their models is that widely known or well established results, surprisingly, should be easier to retract, since their retraction is more relevant to more scientists. Our results support this conclusion. We find that highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.

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Item Type:	Preprint
Creators:	Creators Email ORCID Memon, Shahan Ali samemon@uw.edu West, Jevin jevinw@uw.edu O'Connor, Cailin cailino@uci.edu
Keywords:	retraction, models, data, data science, sociology of science, metascience
Subjects:	General Issues > Data General Issues > Computer Simulation General Issues > Models and Idealization General Issues > Science and Society General Issues > Science and Policy General Issues > Social Epistemology of Science
Depositing User:	Dr. Cailin O'Connor
Date Deposited:	22 Apr 2025 12:37
Last Modified:	22 Apr 2025 12:37
Item ID:	25123
Subjects:	General Issues > Data General Issues > Computer Simulation General Issues > Models and Idealization General Issues > Science and Society General Issues > Science and Policy General Issues > Social Epistemology of Science
Date:	2025
URI:	https://philsci-archive.pitt.edu/id/eprint/25123

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