Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Are female scientists underrepresented in self-correcting science for honest error?
0
Zitationen
6
Autoren
2022
Jahr
Abstract
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, exposure of one’s own failures is not a trivial matter for researchers. However, self-correcting data, results and/or conclusions has increasingly been perceived as 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 is 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, letters, and others, from the Retraction Watch Database (2010-2021). We screened the dataset collected for research articles retracted for honest error, excluding retractions by publishers, editors, or third parties, and those mentioning any investigation issues. We then categorized the records according to country, field, and gender, after selecting research articles with a sole corresponding author. 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.
Ähnliche Arbeiten
International Journal of Scientific and Research Publications
2022 · 2.691 Zit.
Student writing in higher education: An academic literacies approach
1998 · 2.513 Zit.
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling
2012 · 2.316 Zit.
How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data
2009 · 1.925 Zit.
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
2023 · 1.858 Zit.