Organismes communautaires en employabilité et nouveaux immigrants à Montréal : quel est l’apport des services offerts ?
Bibliographic record
Abstract
Cet article traite de l’impact des services offerts aux nouveaux immigrants issus des minorités visibles par les organismes communautaires (OC) qui interviennent dans le domaine de l’employabilité. Nous passons d’abord en revue les principales difficultés que rencontrent généralement les nouveaux immigrants issus des minorités visibles dans leur processus d’insertion sur le marché du travail. Nous présentons ensuite quelques éléments de réflexion sur la contribution des organismes communautaires en employabilité à l’insertion des immigrants dans leur société d’accueil. Nous mettons enfin en lumière les effets des services de ce type d’OC sur l’intégration des nouveaux immigrants issus des minorités visibles à Montréal. Nous montrons que les OC facilitent l’intégration de ces nouveaux immigrants non seulement sur le marché du travail, mais aussi dans des réseaux sociaux et que l’apport des services des OC n’est pas significativement différent selon le sexe de l’usager.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".