Bibliographic record
Abstract
Selim Berker soutient que le constructivisme humien implique que nos jugements à propos de nos propres raisons (morales) sont fiables, mais que nos jugements à propos des raisons des autres ne le sont pas. C’est ce que l’on peut appeler le problème de l’asymétrie. Une manière pour les constructivistes de résoudre ce problème consisterait à soutenir que des facteurs évolutionnaires expliquent que nous partageons certaines raisons. Dès lors, si nos jugements à propos de nos propres raisons sont fiables, nos jugements à propos des raisons des autres le sont également puisque nous avons (presque) tous les mêmes raisons. Dans cet article, je soutiens que les arguments de Berker à l’encontre de cette solution ne sont pas décisifs, mais qu’il y a néanmoins de sérieux obstacles à sa mise en oeuvre considérant ce que nous enseignent les sciences biologiques. Je propose une version améliorée de la solution que les constructivistes pourraient développer.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".