Advocate cultivation of academic ethics: why is it necessary?
Bibliographic record
Abstract
We teach and practice ethical behavior with all clinical and research activities. Notably, we are well educated to treat the subjects participating in research studies with high ethical standards. However, the ethics of interacting with colleagues, or with junior faculty members, are neither well defined nor taught. Dealing with junior faculty has parallels to dealing with vulnerable research subjects such as children, mentally or physically challenged groups, prison inmates or army recruits. Like any other vulnerable population, lower-ranking faculty members are often at the mercy of department chairs or other higher-ranked faculty members. Herein we present some potentially unethical or unfair examples related to academic research. Our goal is to educate the academic community of conceptual paths and to prevent similar untoward occurrences from happening in the future. Unethical behaviors related to sexual misconduct have already been described elsewhere and are not included in this manuscript.
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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 | 0.059 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".