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Record W4395480019 · doi:10.1515/9782760556362

La francophonie dans les politiques publiques au Canada

2022· book· fr· W4395480019 on OpenAlexaboutno aff
Isabelle Caron

Bibliographic record

VenuePresses de l'Université du Québec eBooks · 2022
Typebook
Languagefr
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Plus de 50 ans après l’adoption de la Loi sur les langues officielles qui institua le français et l’anglais comme langues officielles au Canada, la place accordée au français et à la francophonie sur l’ensemble du territoire canadien demeure un sujet d’actualité qui met constamment en lumière les défis rencontrés par les francophones au pays. Cet ouvrage porte un regard sur les enjeux de la francophonie et sa place dans les politiques publiques au Canada. Il nous permet de constater que la question de la francophonie est constamment reléguée au second plan des priorités en matière de politiques publiques, que ce soit en ce qui a trait aux relations intergouvernementales, aux formes émergentes de la francophobie, à l’exclusion de la langue comme facteur identitaire dans l’ACS+, aux communications publiques relatives à la COVID-19, à l’insécurité alimentaire au Nouveau-Brunswick, aux Forces armées canadiennes ou encore aux politiques publiques ontariennes. S’inscrivant dans un contexte politique où le gouvernement fédéral et le gouvernement du Québec manifestent simultanément leur désir d’accroître les mesures pour protéger la francophonie au Canada et soutenir son essor, ce livre se veut le point de départ d’une réflexion qui invite les décideurs publics à ramener la francophonie au premier rang dans l’élaboration et la mise en œuvre des politiques publiques.

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)0.004
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 score0.108
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.006
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.205
Teacher spread0.195 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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