Contribution of practical wisdom to resolving ethical issues
Bibliographic record
Abstract
Evaluators often find themselves on ‘rough ground’ as they try to do the right thing and do it well. They face unanticipated ethical issues requiring decisions that are subtle and nuanced, and they do not always find the expected guidance in current ethics guidelines. In the present article, the authors offer practical wisdom as an alternative to the deontological approach. First, we develop this complex concept and illustrate its contribution to addressing and resolving ethical issues. Second, we present a study and the model that emerged from it to illustrate how practical wisdom contributes to finding solutions to ethical dilemmas and taking the necessary action.
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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 | 0.145 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.010 | 0.091 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.013 | 0.015 |
| 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; 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".