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Record W3002967970

How long do sub-Saharan migrants take to settle in France

2016· article· fr· W3002967970 on OpenAlexaboutno aff
Anne Gosselin, Annabel Desgrées du Loû, Éva Lelièvre, Rosemary Dray‐Spira, Nathalie Lydié

Bibliographic record

VenueArchined · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicMigration, Identity, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceQuarter (Canadian coin)Demographic economicsPeriod (music)DemographyPersonal incomeGeographySocioeconomicsEconomic growthEconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

Six à sept ans après leur arrivée en France, la moitié des migrants d’Afrique subsaharienne n’ont toujours pas les rois éléments d’installation que sont un titre de séjour d’au moins un an, un logement personnel, et un travail. Au bout de onze à douze ans, c’est encore le cas d’un quart d’entre eux. Cette longue période de précarité après l’arrivée en France tient plus aux conditions d’accueil (longueur du processus de régularisation, marché du travail segmenté, discriminations) qu’aux caractéristiques individuelles des arrivants. La situation des migrants subsahariens finit par se stabiliser, mais pour beaucoup d’entre eux, c’est au prix du passage par une longue période d’insécurité.

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 score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.304
Teacher spread0.282 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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