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Record W2559654892 · doi:10.1177/1354068816678881

Multiculturalism, political parties, and the conflicting pressures of ethnic minorities and far-right parties

2016· article· en· W2559654892 on OpenAlexaff
Daniel Westlake

Bibliographic record

VenueParty Politics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMulticulturalismMainstreamEthnic groupPolitical sciencePoliticsPolitical economyFar rightContext (archaeology)SalientLawSociologyGeography

Abstract

fetched live from OpenAlex

Multiculturalism is an increasingly salient election issue. The growing size of many countries’ ethnic minority populations pushes parties to support multiculturalism, whereas the emergence of far-right parties in many countries pressures them to oppose it. This article examines parties’ positions on multiculturalism in a comparative context. It looks at 19 countries including most of Western Europe, North America, Australia, New Zealand, and Japan. It argues that the influence of ethnic minorities over parties depends on electoral systems, and the strategies mainstream parties adopt in response to the far-right. The article finds that increases in ethnic minorities’ electoral strength lead parties to increase their support of multiculturalism to a greater degree in single-member district electoral systems than in proportional ones. Further, parties co-opt the anti-multicultural positions of far-right parties, and right parties do so more than left parties.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.342
Teacher spread0.292 · 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 designObservational
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

Citations34
Published2016
Admission routes1
Has abstractyes

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