Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Ein externer Link zum Volltext ist derzeit nicht verfügbar.
Teams prevent misconduct: A study of retracted articles from the Web of Science
3
Zitationen
2
Autoren
2019
Jahr
Abstract
Collaborations become increasingly important among almost all scientific disciplines. Teams can be more productive and achieve more attention to their work. It is, however, unclear, whether teams also lead to higher research integrity. On the one hand, there may be a volunteer's dilemma in larger groups such that the responsibility diffuses who is controlling whom. The 'volunteer hypothesis' predicts that the more co-authors, the more scientific misconduct. On the other hand, larger groups may also achieve a higher level of social control. The 'control hypothesis' predicts that the more co-authors, the less scientific misconduct. Retractions are used as an operationalization of scientific misconduct. The data collection comprises of retracted articles from the Web of Science data set. In addition, control groups of non-retracted articles are constructed, using methods known from causal inference and bibliometrics. The analyses demonstrate that larger author groups have a lower retraction probability compared to smaller author groups. This suggests that teams prevent misconduct, most likely by their higher ability to social control. The results indicate that the development towards more and larger research collaborations may have positive macro-level consequences for the system of science.
Ähnliche Arbeiten
International Journal of Scientific and Research Publications
2022 · 2.691 Zit.
Student writing in higher education: An academic literacies approach
1998 · 2.518 Zit.
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling
2012 · 2.320 Zit.
Comparison of Two Methods to Detect Publication Bias in Meta-analysis
2006 · 2.209 Zit.
How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment
2023 · 1.976 Zit.