Niveaux annuels d'immigration et gains initiaux des immigrants au Canada
Bibliographic record
Abstract
Le niveau annuel d'immigration est l'une des composantes les plus importantes de la politique d'immigration d'un pays. Il est difficile de comparer directement les couts et les avantages de la modification des niveaux d'immigration, car l'immigration peut servir a repondre a plusieurs objectifs. Toutefois, certains effets definis de facon etroite peuvent faire l'objet d'une evaluation empirique. La presente etude porte uniquement sur l'influence possible des niveaux d'immigration sur les gains initiaux des immigrants. La presente etude porte sur l'effet des niveaux d'immigration sur un aspect des resultats sur le marche du travail des immigrants, a savoir leurs gains initiaux, c'est-a-dire leurs gains au cours de leurs deux premieres annees completes au Canada. Une augmentation de l'offre de main-d'oeuvre, c'est-a-dire une cohorte plus nombreuse de nouveaux immigrants, pourrait faire augmenter la concurrence pour les types d'emploi que recherchent les nouveaux immigrants et exercer une pression a la baisse sur les salaires des immigrants compris dans cette cohorte.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".