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Record W2782146790 · doi:10.4000/rdlc.2603

Apprendre et enseigner le français dans l’Ontario, Canada : entre dualité linguistique et réalités plurielles et complexes

2013· article· fr· W2782146790 on OpenAlexaffabout
Julie Byrd Clark, Sylvie A. Lamoureux, Sofia Stratilaki

Bibliographic record

VenueRecherches en didactique des langues et des cultures · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Depuis quarante ans, le Canada est reconnu et célébré comme un pays officiellement bilingue et multiculturel avec ses politiques linguistiques et éducatives qui maintiennent la promotion de cette dualité. Cependant, les textes officiels récents promeuvent toujours des idéologies monolingues relevant des relations étroites entre langue et nation, héritage des années 60 et 70. Dans l’Ontario, on relève deux conséquences majeures : a) refus de légitimer les pratiques langagières des francophones vivant en dehors du Québec, dans des contextes où le français est une langue minoritaire et minorée. b) manque effectif d’un soutien officiel des autorités de l’Ontario pour le développement des répertoires plurilingues et des identités plurielles chez ses citoyens. Une exemplification est proposée avec des extraits de discours de jeunes Canadiens plurilingues, pluriethniques et francophones dans le postsecondaire, extraits de leur biographie langagière.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.682

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.010
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.067
GPT teacher head0.345
Teacher spread0.278 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

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