Écarts salariaux entre francophones et anglophones à Montréal au 19e siècle
Bibliographic record
Abstract
Notre étude fournit une perspective historique à la question des écarts de revenus entre francophones et anglophones en retournant loin en arrière, dans le Montréal du début du 19esiècle. Nous avons mis l’accent sur le marché des apprentis. Nous avons utilisé les détails présents dans les contrats signés entre maîtres et apprentis afin d’isoler les différences de rémunération entre ethnies. Nos résultats montrent des écarts ethniques considérables dans la composition et dans le niveau de rémunération. La plupart des résultats indiquent une prime « anglophone ». Nous constatons également un déclin de la pénalité subie par les francophones, mais cette tendance prend une direction opposée vers la fin des années 1830. Finalement, notre étude montre que la plupart des écarts sont associés au groupe ethnique du maître et non à celui de l’apprenti.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".