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Record W7135801463

Multiculturalism Policy in Canada: Exploring the Dispute with Québec through Framing Analysis.

2023· dissertation· en· W7135801463 on OpenAlexaboutno aff
Hannah Mae Hrynuik Breedon

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)MulticulturalismCriticismOpposition (politics)Government (linguistics)Frame analysis
DOInot available

Abstract

fetched live from OpenAlex

Multiculturalism has been an official federal government policy in Canada since 1971 and is strongly associated with the country. However, from its inception, it has been met with strong criticism and opposition in the predominantly French-speaking province of Québec. While the long history of French-English conflicts in Canada has been explored, there is a paucity of comprehensive literature that focuses on the resurgence of this particular dispute in the last decade. This period includes Québec's adoption of several new, high profile, and controversial policies and laws that mark a rejection of the federal policy. To help fill this gap, and develop a more specific and contemporary description of this dispute, this paper uses frame analysis as a lens through which to examine a range of sources including news articles, government documents, press releases, speeches and interviews. Through this analysis six frames used by federal and provincial actors are identified and discussed: "Unique Cultural Preservation", "Québecois as Dominant Culture" and "Provincial Autonomy" on one side; and "Cultural Diversity and Pluralism", "Accommodation" and "National Unity" on the other. The findings reveal that the dispute is rooted in a historic struggle for power between the province and the federal government...

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)0.004
metaresearch head score (Gemma)0.007
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 score0.773
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0300.011
Scholarly communication0.0120.003
Open science0.0020.003
Research integrity0.0020.002
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 head0.016
GPT teacher head0.266
Teacher spread0.251 · 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
Published2023
Admission routes1
Has abstractyes

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