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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 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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 teacher head, 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

Citations0
Published2016
Admission routes1
Has abstractyes

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