Frontiers in Research Metrics and Analytics (Jan 2023)
Are female scientists underrepresented in self-retractions for honest error?
Abstract
Retractions are among the effective measures to strengthen the self-correction of science and the quality of the literature. When it comes to self-retractions for honest errors, exposing one's own failures is not a trivial matter for researchers. However, self-correcting data, results and/or conclusions has increasingly been perceived as a good research practice, although rewarding such practice challenges traditional models of research assessment. In this context, it is timely to investigate who have self-retracted for honest error in terms of country, field, and gender. We show results on these three factors, focusing on gender, as data are scarce on the representation of female scientists in efforts to set the research record straight. We collected 3,822 retraction records, including research articles, review papers, meta-analyses, and letters under the category “error” from the Retraction Watch Database for the 2010–2021 period. We screened the dataset collected for research articles (2,906) and then excluded retractions by publishers, editors, or third parties, and those mentioning any investigation issues. We analyzed the content of each retraction manually to include only those indicating that they were requested by authors and attributed solely to unintended mistakes. We categorized the records according to country, field, and gender, after selecting research articles with a sole corresponding author. Gender was predicted using Genderize, at a 90% probability threshold for the final sample (n = 281). Our results show that female scientists account for 25% of self-retractions for honest error, with the highest share for women affiliated with US institutions.
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