Diminution de l'avantage des immigrants en matiere de propriete : analyse de cycle de vie de la baisse de fortunes et de l'evolution de l'accession a la propriete chez les immigrants de Montreal, Toronto et Vancouver, 1981 a 2001
Bibliographic record
Abstract
Par le passe, les familles d'immigrants en age de travailler dans les grandes agglomerations urbaines du Canada presentaient des taux de propriete de l'habitation superieurs a ceux de la population de souche. Ces 20 dernieres annees cependant, l'avantage est passe a la population de souche a cause tant d'une baisse des taux d'immigration que d'une hausse des taux de propriete chez les Canadiens nes au pays. Mon propos sera d'evaluer l'efficacite des modeles standard des choix de consommation avec des indicateurs comme ceux de l'age, du revenu, de la scolarite et du type de familles et avec plusieurs caracteristiques des immigrants comme elements d'explication de cette evolution. Je constaterai que le modele standard explique presque entierement l'avantage dont jouissaient les immigrants en matiere de propriete en 1981, ainsi que la montee des taux de propriete au fil des ans dans la population de souche. Le modele standard explique toutefois le tiers seulement en gros de l'evolution des taux de 1981 a 2001 dans la population immigrante meme apres prise en compte de la baisse bien connue des fortunes economiques des immigrants. J'examinerai certaines des consequences de cette sous-explication et ferai plusieurs recommandations de recherches complementaires.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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, 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".