Teaching Applied Ethics, Critical Theory, and “Having to Brush One’s Teeth”
Bibliographic record
Abstract
Dans cet article, l'auteur enumere les objectifs de son cours d'ethique appliquee. Il s'agit d'abord de developper le sens critique des eleves afin de favoriser les echanges et de valoriser l'argumentation. Les dialogues doivent ensuite s'accompagner d'une reflexion plus profonde sur le sens de ces discussions ethiques en rapport avec leur propre vie. A terme, les aspects positifs de ce cours peuvent s'elargir a l'ensemble de la classe en premier lieu, dans le cadre des relations interpersonnelles qui seraient alors accompagnees des notions de respect et de confiance. L'exemple donne ici reprend les themes du harcelement sexuel et de la discrimination positive.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".