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Record W4387219950 · doi:10.1177/01925121231156633

Marginalized, but not demobilized: Ethnic minority protest activity when facing discrimination

2023· article· en· W4387219950 on OpenAlexafffundabout
Antoine Bilodeau, Stephen White, Clayton Ma, Luc Turgeon, Ailsa Henderson

Bibliographic record

VenueInternational Political Science Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of OttawaCarleton UniversityConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnic groupContext (archaeology)PoliticsDiversity (politics)Political scienceBacklashGender studiesSocial psychologySociologyPsychologyLaw

Abstract

fetched live from OpenAlex

In a context of backlash against diversity in many countries, we know little about how ethnic minorities respond politically when they personally experience discrimination. Moving beyond the study of electoral participation, this research investigates whether experiences of discrimination push ethnic minorities toward an alternate political pathway for those who feel sidelined by the political community: protest activity. The study also examines whether the context of discrimination (i.e. public or private sphere) has different consequences for protest participation, and whether intragroup contact enhances the effects of discrimination on protest participation. Relying on a survey of 1647 respondents from racialized backgrounds in Canada, our findings indicate that discriminatory experiences increase participation in protest activities irrespective of its context, and that the positive relationship between discriminatory experiences and protest activity is stronger among respondents with greater intragroup contact.

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)0.001
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.186
GPT teacher head0.465
Teacher spread0.279 · 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

Citations10
Published2023
Admission routes3
Has abstractyes

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