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L’accueil des personnes déplacées ukrainiennes au Québec

2025· article· fr· W4409992937 on OpenAlexaboutno aff
Adèle Garnier

Bibliographic record

VenueRevue Possibles · 2025
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Cet article aborde l’accueil des personnes déplacées ukrainiennes au Québec ayant fui l’invasion et agression russe de 2022. Alors que le Canada accueille la deuxième plus grande diaspora ukrainienne au monde, un statut migratoire temporaire novateur a été créé par le gouvernement fédéral pour admettre les déplacé·es ukrainien·nes et leur donner accès aux services d’établissement – ce qui n’est pas le cas pour les autres migrant·es temporaires. Le Québec s’est rapidement mobilisé pour soutenir les Ukrainien·nes. Dans la province, qui gère ses services d’établissement de manière autonome, les migrant·es temporaires étaient déjà éligibles aux services d’établissement depuis 2020. Les déplacé·es ukrainien·nes du Québec ont un profil démographique et socioéconomique similaire à ceux du reste du Canada. Au niveau local, ils font face aux mêmes défis qu’ailleurs au pays, par exemple l’inégale densité de la diaspora ukrainienne et des services de première ligne, mais aussi à des défis spécifiques tels que le rôle de la maîtrise du français dans l’admissibilité à la résidence permanente.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.134
GPT teacher head0.450
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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