What Intellectual Ethics for Contemporary Science? Perspectives of Virtue Epistemology
Bibliographic record
Abstract
In face of unethical incidents that threaten the world of science, a question of the necessity and a possible shape of intellectual ethics has been raised. The article argues that advantages of virtue epistemology make it more attractive than other models of intellectual ethics (deontology, in particular). To that purpose, it reviews alternative models for intellectual ethics, analyses and criticises deontological approach and demonstrates the virtues of the virtue approach. As problems with implementation of virtue ethics have been put against that approach, the article addresses the question of how to promote virtue intellectual ethics. It discusses four possible methods of formation in virtues: theoretical, success-oriented, social and based on emulating exemplars. It argues for the role of excellent exemplars (both direct and narrative) whose emulation forms virtues in agent. The conclusions of the article should transform the way we think about intellectual ethics and promote it.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.079 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".