Bibliographic record
Abstract
BACKGROUND: Research ethics boards (REBs) exist for good reason. By setting rules of ethical behaviour, REBs can help mitigate the risk of researchers causing harm to their research participants. However, the current method by which REBs promote ethical behaviour does little more than send researchers into the field with a set of rules to follow. While appropriate for most situations, rule-based approaches are often insufficient, and leave significant gaps where researchers are not provided institutional ethical direction. RESULTS: Through a discussion of a recent research project about drinking and driving in South Africa, this article demonstrates that if researchers are provided only with a set of rules for ethical behaviour, at least two kinds of problems can emerge: situations where action is required but there is no ethically good option (zungzwang ethical dilemmas) and situations where the ethical value of an action can only be assessed after the fact (contingent ethical dilemmas). These dilemmas highlight and help to articulate what we already intuit: that a solely rule-based approach to promoting ethical research is not always desirable, possible, effective, or consistent. CONCLUSIONS: In this article, I argue that to better encourage ethical behaviour in research, there is a need to go beyond the rules and regulations articulated by ethics boards, and focus more specifically on creating and nurturing virtuous researchers.
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 | 0.190 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.109 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".