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Record W2606498808 · doi:10.1017/s0008423915000517

The Politics of Language Roadmaps in Canada: Understanding the Conservative Government's Approach to Official Languages

2015· article· en· W2606498808 on OpenAlexafffundabout
Linda Cardinal, Helaina Gaspard, Rémi Léger

Bibliographic record

VenueCanadian Journal of Political Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsSimon Fraser UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGovernment (linguistics)Language policyPublic administrationPolitical scienceCharterPoliticsCitizenshipRepresentation (politics)LawSociologyLinguistics

Abstract

fetched live from OpenAlex

Abstract This article critically examines the Conservative government's approach to official languages, through a policy instrument framework. Special attention is paid to the third federal roadmap for official languages—the first having been unveiled by the Liberal government in 2003 and the second by the Conservative minority government in 2008—and how this roadmap conveys a new representation of official languages in relation to Canadian identity and citizenship. The focus on the linguistic integration of new immigrants in the 2013 language roadmap generates interest. The policy instrument framework also shows how language roadmaps represent the fourth generation of official language policies in Canada; the first three generations found their respective bases in the 1969 Official Languages Act , the Charter of Rights and Freedoms and the 1988 Official Languages Act . The article concludes that an analysis of language roadmaps elucidates transformations initiated by the Conservative governments in the area of official languages in Canada. It also promotes further exploration and analysis of language policies through the policy instrument framework.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.120
GPT teacher head0.406
Teacher spread0.286 · 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 designTheoretical or conceptual
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

Citations17
Published2015
Admission routes3
Has abstractyes

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