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Record W2809799869

La Politique de sélection des immigrants du Québec : Un modèle enviable en péril

2012· book· fr· W2809799869 on OpenAlexaboutno aff
Laurence Monnot

Bibliographic record

VenueEditions Hurtubise eBooks · 2012
Typebook
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Le Quebec choisit ses immigrants en fonction de criteres et en vue d'objectifs qu'il definit lui-meme. Cette autonomie, conquise de haute lutte dans les annees 1960 et 1970, n'a d'equivalent ni dans un autre pays, ni dans une autre province du Canada. En 2011, 37 000 travailleurs qualifies ont ete selectionnes selon leur âge, leur formation, leur experience de travail, leurs competences linguistiques et le profil de leur conjoint. La responsabilite de relever le defi de l'immigration a ete confiee a un acteur principal, le ministere de l'Immigration. Depuis quarante-cinq ans, et bien qu'il ait toujours ete le parent pauvre de la distribution budgetaire, ce ministere a developpe un savoir-faire et des traditions non negligeables. La coherence a long terme de la politique quebecoise peut faire figure de modele. Pourtant, elle est aujourd'hui menacee. Parce que l'immigration et la selection des candidats font l'objet de questionnements, certains sembleraient prets a faire table rase de cette expertise pour, a l'instar du federal, ne plus river la selection qu'a un seul objectif?: l'employabilite a court terme. Or, la selection de futurs concitoyens doit se soucier de leur integration au regard des caracteristiques economiques, culturelles et linguistiques propres a la societe quebecoise. Il importe donc de conserver des criteres pluriels, comme d'assurer la qualite du processus de selection. Cet ouvrage retrace l'histoire des politiques d'immigration quebecoises. Il expose aussi les rouages de la politique de selection des immigrants du Quebec, ses objectifs parfois contradictoires, ses moyens et ses limites. De plus, il propose quelques pistes pour relever les defis a venir.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.533
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.004

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 head0.087
GPT teacher head0.381
Teacher spread0.295 · 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
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

Citations0
Published2012
Admission routes1
Has abstractyes

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