Bibliographic record
Abstract
« Nous avons ici une des présentations faites à l’occasion des conférences annuelles Chevrette-Marx, consacrées soit aux principes fondamentaux, soit au fédéralisme ou encore aux libertés publiques. Le titre de l’ouvrage de Danielle Pinard, Les faits alternatifs du droit constitutionnel, nous amène à penser aux interventions devant les médias de Kellyanne Conway, conseillère de l’actuel président des États-Unis. L’auteure y fait allusion pour expliquer que ce sont là des éléments faisant appel à l’émotion et à des opinions personnelles plutôt qu’à des faits vérifiables. Elle se pose ainsi les questions suivantes : Les faits sur lesquels les lois s’appuient souvent sont-ils véridiques? Les conclusions que les législateurs tirent de « faits » pourraient-elles n’être que de simples approximations? […] »
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".