La mesure de l’impact économique de l’immigration internationale. Problèmes méthodologiques et résultats empiriques
Bibliographic record
Abstract
Les politiques d’immigration sont presque toujours fondées sur le postulat selon lequel l’apport de nouveaux immigrants ne peut que contribuer positivement à la croissance du revenu par habitant et, plus généralement, aux conditions économiques du pays d’accueil. L’objet de cet article est d’examiner comment l’on peut vérifier la validité de ce postulat, et d’évaluer les résultats obtenus par les études empiriques qui ont tenté de mesurer l’impact économique de l’immigration internationale. La première section est consacrée à la définition de la problématique. Dans les quatre sections suivantes, l’auteur examine les quatre grandes approches méthodologiques que l’on peut adopter pour mesurer cet impact, et produit, pour chacune de ces approches, quelques résultats empiriques. Une brève conclusion permet de dégager les principales implications méthodologiques et politiques des résultats obtenus.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".