Les répercussions du 11 septembre 2001 sur l’établissement économique des nouveaux immigrants au Canada et au Québec
Bibliographic record
Abstract
Quelles répercussions les événements qui se sont produits au World Trade Center de New York le 11 septembre 2001 ont-ils eues sur l’établissement en emploi des nouveaux immigrants au Canada ? Alors qu’on se serait attendu à une incidence différenciée selon l’origine ethnique, défavorable aux personnes d’origine arabe à cause de la stigmatisation de ce groupe par l’opinion publique, l’analyse ne révèle pas de tels effets. On se retrouve plutôt face à un ralentissement du marché de l’emploi à son resserrement pour les immigrants les plus jeunes et les plus vieux, ralentissement qui se présente de façon différente au Québec et dans le reste du Canada. Cette analyse est possible car la première vague d’entrevues de l’Enquête longitudinale auprès des immigrants du Canada (ELIC) de Statistique Canada était en cours en septembre 2001 (n = 12 040). Les analyses sont réalisées à l’aide de régressions de survie semi-paramétriques.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".