Are Men More Likely than Women To Commit Scientific Misconduct? Maybe, Maybe Not
Bibliographic record
Abstract
In their study published in January 2013 in mBio, Fang et al. reviewed records from the Office of Research Integrity (ORI) and found more cases of scientific misconduct committed by men than women, particularly by faculty (F. C. Fang, J. W. Bennett, and A. Casadevall, mBio 4:1-3, 2013). Powerful social norms shape the way men and women behave, and implicit gender schemas can lead to different evaluation standards for men and women for tasks stereotypically linked to one gender. It is possible that norms for acceptable male and female behavior could lead to a lower threshold for men than women to engage in the risky behavior of scientific misconduct. It is also possible that women and men commit scientific fraud at the same rate but that, because crime is a male-gendered domain, evaluators require more proof of the criminal "competence" of women for an investigation to rise to the level of an ORI case or that female gender norms for likeability and a lower apology threshold more often prevent escalation of women's fraud beyond a local level. Male scientists also have more opportunity to commit fraud than female scientists because they receive more NIH research funding--a finding that may also be influenced by gender schemas. We cannot conclude from the ORI data that men are more likely than women to risk the consequences of committing scientific misconduct simply because risk taking aligns with male gender stereotypes. Neither can we conclude that because men are more likely than women to commit fraud in other contexts, men are also more likely than women to commit scientific fraud. We can conclude, however, that scientific misconduct, regardless of who commits it, diminishes all who contribute to the scientific enterprise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".