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Record W1506955139

Enclaves de minorites visibles dans les quartiers et resultats sur le marche du travail des immigrants

2003· preprint· fr· W1506955139 on OpenAlexaboutno aff
Feng Hou, Garnett Picot

Bibliographic record

VenueRePEc: Research Papers in Economics · 2003
Typepreprint
Languagefr
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesImmigrationArt
DOInot available

Abstract

fetched live from OpenAlex

A l'aide des donnees des recensements de 1981, 1986, 1991 et 1996, cette etude permet d'examiner le rapport entre le fait de vivre dans une enclave de minorite visible et les resultats sur le marche du travail des immigrants dans les trois plus grandes villes au Canada. Les resultats montrent que le nombre de ces enclaves, definies comme des secteurs de recensement dont la population comprend au moins 30 % de membres d'une meme minorite visible (soit des Chinois, des habitants de l'Asie du Sud ou des Noirs), est passe de 6 en 1981 a 142 en 1996, cette augmentation se produisant surtout a Toronto et a Vancouver. L'association entre l'exposition a des voisins membres du meme groupe et l'emploi est parfois negative, mais generalement non significative. L'association entre l'exposition a des voisins membres du meme groupe et l'emploi dans une profession cloisonnee est positive, mais souvent peu significative. Le rapport entre l'exposition et les gains provenant d'un emploi est tres faible. Toutefois, on constate certaines differences importantes entre les groupes. L'association entre l'exposition a des voisins membres du meme groupe et les resultats sur le marche du travail est de facon generale tres faible chez les immigrants chinois, mais souvent negative et forte chez les immigrants noirs.

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 score0.718
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.060
GPT teacher head0.328
Teacher spread0.268 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicMigration, Ethnicity, and EconomyFrench-language works237,207