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Record W2967088108 · doi:10.5964/jspp.v7i1.673

The case for and causes of intraminority solidarity in support for reparations: Evidence from community and student samples in Canada

2019· article· en· W2967088108 on OpenAlexafffundabout
Katherine B. Starzyk, Katelin H. S. Neufeld, Renée El‐Gabalawy, Gregory D. Boese

Bibliographic record

VenueJournal of Social and Political Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser UniversityUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaAustralian GovernmentUniversity of Manitoba
KeywordsIndigenousSolidarityEthnic groupGovernment (linguistics)Context (archaeology)Political scienceWhite (mutation)Gender studiesSociologyPoliticsSocial psychologyCriminologyPsychologyLawGeography

Abstract

fetched live from OpenAlex

In three studies, we examined how racial/ethnic majority (i.e., White) and non-Indigenous minority participants in Canada responded to reparations for Indigenous peoples in Canada. Our goal was to understand whether and why there may be intraminority solidarity in this context. In Study 1, with a large, national survey (N = 1,947), we examined the extent to which participants agreed the government should be responsible for addressing human rights violations committed by previous governments as well as whether the government has done enough to address the wrongs committed against Indigenous peoples in Canada. With a sample of undergraduate students in Study 2 (N = 144) and another community sample in Study 3 (N = 233), we examined possible mediators of the relationship between ethnic status and support for reparations. Taken together, the results of three studies suggest that, compared to White majority Canadians, non-Indigenous minority Canadians were more supportive of providing reparations to Indigenous peoples through a complex chain of collective victimhood, inclusive victim consciousness, continued victim suffering, and solidarity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.258
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.125
GPT teacher head0.470
Teacher spread0.345 · 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 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

Citations27
Published2019
Admission routes3
Has abstractyes

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