Réformes de l'immigration au Québec en 2019 et 2020: La logique politique à l’épreuve de l'analyse statistique
Bibliographic record
Abstract
This research note examines the economic performance of economic immigrants selected under the Quebec Experience Program (PEQ). Launched in 2010 and currently being challenged by the Government of Quebec, this immigration program offers an accelerated path to obtaining a Quebec Selection Certificate. Drawing on data from The Longitudinal Immigration Database (IMDB), this analysis focuses on PEQ immigrants' employment rates and employment income compared to the Quebec population aged 25 and 44, other economic immigrants admitted to Quebec, and candidates for the Canadian experience program admitted in another province. The results show that the first cohorts of this program performed very well on the Quebec job market, a performance that compares favorably with that of the other groups studied. The results indicate that the economic arguments put forward by the Québec Government to justify the reform of the PEQ do not withstand the statistical examination of employment outcomes; other factors should justify this reform.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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, 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".