Disparités sociales et disparités régionales : l’exemple du Québec
Bibliographic record
Abstract
Une grande confusion entoure encore la notion de disparités régionales au Québec ; l'identification des régions pose des problèmes, la mesure du bien-être régional est au mieux arbitraire, et l'analyse sociale est faite indépendamment de l'analyse régionale. En deçà des problèmes de perception et d'identification régionale, il y a ceux plus criants des comparaisons conjointes dans la dispersion interpersonnelle et interrégionale du revenu monétaire. À l'aide des données du ministère fédéral du Revenu, pour les années 1966 et 1974, il est établi ici que la dispersion interpersonnelle des revenus s'est maintenue entre ces deux dates, pendant que la dispersion interrégionale diminuait de façon sensible. Cette analyse débouche sur une critique du véritable rôle social des institutions gouvernementales à vocation spatiale directe et explicite.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".