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Record W2767444892 · doi:10.4000/books.pum.2587

La fédéralisation de l'immigration au Canada

2016· book· fr· W2767444892 on OpenAlexaboutno aff
Mireille Paquet

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2016
Typebook
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceImmigrationPhilosophyLaw

Abstract

fetched live from OpenAlex

Au Canada, le dossier de l’immigration a toujours été sous la responsabilité du gouvernement fédéral, jusqu’à ce que le Québec, dans les années 1960, exige plus de pouvoirs à ce chapitre. Il faudra quelques décennies pour que toutes les provinces entrent dans la ronde et s’occupent sérieusement de cette question, mais de 1990 à 2010, on assiste bel et bien à la fédéralisation progressive de la gouvernance de l’immigration. Et même si le Canada maintient son approche générale envers les nouveaux arrivants, les dix provinces n’en élaborent pas moins des stratégies officielles d’immigration et appliquent diverses politiques de sélection et d’intégration. Par le recours à une analyse combinant plus de 70 entretiens et de nombreux documents gouvernementaux et d’archives, le présent ouvrage montre que la fédéralisation est en grande partie le résultat de la mobilisation des provinces. Par leur action et leurs revendications, ces dernières ont entraîné une restructuration considérable de l’architecture fiscale, économique et politique du fédéralisme canadien. Elles ont aussi grandement contribué à redéfinir les responsabilités, les capacités et les rôles respectifs des gouvernements auprès de leur population.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.099
Threshold uncertainty score0.715

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0110.003
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations36
Published2016
Admission routes1
Has abstractyes

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