Bibliographic record
Abstract
Although medical centres have established boards, special committees, and offices for the review and redress of breaches in ethical behaviour, these mechanisms repeatedly prove themselves ineffective in addressing research misconduct within the institutions of academic medicine. As the authors see it, institutional design: (1) systematically ignores serious ethical problems, (2) makes whistleblowers into institutional enemies and punishes them, and (3) thereby fails to provide an ethical environment. The authors present and discuss cases of academic medicine failing to address unethical behaviour in academic science and, thereby, illustrate the scope and seriousness of the problem. The Olivieri/Apotex affair is just another instance of academic medicine's dereliction in a case of scientific fraud and misconduct. Instead of vigorously supporting their faculty member in her efforts to honestly communicate her findings and to protect patients from the risks associated with the use of the study drug, the University of Toronto collaborated with the Apotex company's "stalling tactics," closed down Dr Olivieri's laboratory, harassed her, and ultimately dismissed her. The authors argue that the incentives for addressing problematic behaviour have to be revised in order to effect a change in the current pattern of response that occurs in academic medicine. An externally imposed realignment of incentives could convert the perception of the whistleblower, from their present caste as the enemy within, into a new position, as valued friend of the institution. The authors explain how such a correction could encourage appropriate reactions to scientific misconduct from academic medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.052 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".