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Record W2167204643 · doi:10.7202/000372ar

Offre de travail des femmes mariées immigrantes au Canada

2009· article· fr· W2167204643 on OpenAlexaffvenueabout
Brahim Boudarbat, Sonia Ines Gontero

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languagefr
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversité de MontréalCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Cette étude analyse l’offre de travail des femmes mariées selon le statut d’immigration au Canada. Nos résultats empiriques obtenus à l’aide des données du recensement canadien de 2001, indiquent que les femmes immigrantes ont un taux d’activité plus faible comparativement aux natives. Les immigrantes provenant d’Asie sont les moins susceptibles de participer au marché du travail alors que, parallèlement, cette région est devenue la source d’immigration la plus importante au Canada. Par ailleurs, nous trouvons que l’élasticité de l’offre de travail par rapport au salaire horaire est deux fois et demie plus élevée pour les natives comparativement aux immigrantes (0,18 contre 0,072). Ce faible degré de réponse des femmes immigrantes aux signaux du marché du travail, pourrait indiquer qu’elles ont moins de choix dans les faits à cause, peut-être, de contraintes culturelles quant à la position de la femme au sein du ménage, ou encore à cause des difficultés d’accès à l’emploi auxquelles font face les immigrantes. Enfin, nous trouvons que les immigrantes européennes ont une élasticité de l’offre de travail par rapport au salaire qui est légèrement supérieure à celle des autres immigrantes (0,082 contre 0,058), mais qui demeure significativement inférieure à celle des natives.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.030
GPT teacher head0.261
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2009
Admission routes3
Has abstractyes

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