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Record W2817694620 · doi:10.5507/ro.2016.015

Immigrant languages and the linguistic situation in Canada

2016· article· en· W2817694620 on OpenAlexaboutno aff
Jaromír Kadlec

Bibliographic record

VenueRomanica Olomucensia · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEthnologyImmigrationPolitical scienceHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

L'article aborde la question de la position des langues immigrantes et de leur impact sur la situation linguistique au Canada. Les vagues d'immigration, l'origine des immigrants et leurs compĂŠtences linguistiques ont toujours eu un impact dĂŠcisif sur la situation linguistique au Canada. Parmi les immigrants qui s'installent au Canada prĂŠvalent pour des raisons historiques, politiques et gĂŠographiques les Asiatiques et la situation linguistique dans le pays change en faveur des langues asiatiques (surtout du chinois). Par contre, le QuĂŠbec accueille moins d'Asiatiques et les immigrants viennent beaucoup plus de l'Afrique, de l'AmĂŠrique du Sud et des Antilles et les immigrants qui arrivent au QuĂŠbec contribuent à l'amĂŠlioration de la position de la langue française dans cette province unilingue francophone.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0100.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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 designQualitative
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

Citations0
Published2016
Admission routes1
Has abstractyes

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