Beyond following rules: Teaching research ethics in the age of the Hoffman Report
Bibliographic record
Abstract
The Hoffman Report scandal demonstrates that ethics is not objective and ahistorical, contradicting the comforting progressive story about ethics many students receive. This modern-day ethical failure illustrates some of the weaknesses of the current ethics code: it is rule-based, emphasizes punishments for noncompliance, and assumes a rational actor who can make tricky ethical decisions using a cost–benefit analysis. This rational emphasis translates into pedagogy: the cure for unethical behavior is more education. Yet such an approach seems unlikely to foster ethical behavior in the real world, either for students or for mature scientists. This article argues for an alternative ethical system and a different way of teaching ethical behavior. Virtue ethics emphasizes the development of ethical habits and traits through regular practice and reflection. We show how virtue ethics complements a feminist approach to science, in which scientists are encouraged to reflect on their own biases, rather than attempting to achieve an impossible objectivity. Our article concludes with pedagogical suggestions for teaching ethical behavior as a practical and intelligent skill.
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 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 | 0.054 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".