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Multiculturalism Policy in Canada

2022· book-chapter· en· W4312928618 on OpenAlexaboutno aff
Keith Banting

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismNationalismPoliticsPolitical sciencePolitical economyConservatismDemocracyGender studiesMainstreamImmigrationSociologyLaw

Abstract

fetched live from OpenAlex

Abstract The year 2021 represents the 50th anniversary of the adoption of multiculturalism in Canada. Clearly, multiculturalism policy has stood the test of time. However, more than sheer longevity is involved. In programmatic terms, multiculturalism has advanced the goals that animated its introduction in 1971. It has helped to change the terms of integration for immigrant communities, laying to rest ideas of assimilation, and creating space for minorities to maintain and celebrate aspects of their culture and traditions while participating in the mainstream of Canadian life. In addition, multiculturalism has been part of a broad state-led redefinition of national identity, helping to build a more inclusive sense of nationalism. Judged by these original goals, the multiculturalism program has met with considerable success. However, multiculturalism has limits. It has not eliminated racial inequality, and the commitment to diversity seems fragile at times, most recently in the case of Muslims. In addition, multiculturalism has been a conflicted political success. The policy is not embedded in a comprehensive political consensus, and potent political challenges have emerged in the name of social conservatism and Québec nationalism. Nonetheless, the policy has had sufficient political support to survive at the national level for half a century. In effect, multiculturalism is a case of conflicted political success and resilient program success. Moreover, judged by the experience of democratic countries generally, Canadian multiculturalism seems even more successful. Perhaps most importantly, the policy has arguably helped to forestall the type of anti-immigrant backlash we have seen elsewhere.

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)0.006
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0290.006
Scholarly communication0.0090.002
Open science0.0030.007
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0200.001

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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
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

Citations6
Published2022
Admission routes1
Has abstractyes

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