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Record W2073398408 · doi:10.1177/0095798409344083

Racial Identity, Racial Context, and Ingroup Status: Implications for Attributions to Discrimination Among Black Canadians

2009· article· en· W2073398408 on OpenAlexaff
H. Robert Outten, Benjamin Giguère, Michael T. Schmitt, Richard N. Lalonde

Bibliographic record

VenueJournal of Black Psychology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsYork UniversitySimon Fraser University
Fundersnot available
KeywordsOptimal distinctiveness theorySocial psychologyPsychologyAttributionRacismRacial formation theoryIdentity (music)Context (archaeology)Ingroups and outgroupsSocial identity theoryRace (biology)IdeologySocial groupGender studiesSociologyPoliticsPolitical science

Abstract

fetched live from OpenAlex

Using Self-Categorization Theory as a conceptual framework for understanding attributions to discrimination, the primary aim of this study was to move beyond focusing on the degree to which racial minorities define themselves in terms of their race (i.e., racial centrality). Specifically, the authors examined how multiple dimensions of Black racial identity affected attributions to racial discrimination in two attributionally ambiguous situations. For Black Canadians exposed to intergroup contexts, racial identity beliefs that emphasize the distinctiveness of the Black experience (low public regard and nationalist ideology) were associated with greater perceived discrimination across the two situations, whereas racial identity beliefs that stress the similarities between the Black experience and that of other groups (assimilationist and humanist ideologies) were associated with perceiving less discrimination. Racial identity beliefs did not predict attributions when the target and potential perpetrator were members of the same racial group. Implications for studying the relationship between Black racial identity and perceived discrimination are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.941

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 communication0.0000.001
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 head0.048
GPT teacher head0.428
Teacher spread0.380 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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