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Record W2418794081 · doi:10.1111/bjso.12145

Social identity change in response to discrimination

2016· article· en· W2418794081 on OpenAlexafffundabout
Cristina Perozzo, Roxane de la Sablonnière, Émilie Auger, Mathieu Caron‐Diotte

Bibliographic record

VenueBritish Journal of Social Psychology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersUniversité de Montréal
KeywordsPsychologySocial psychologySocial identity theoryIdentity (music)Identity changeSocial groupFeelingAesthetics

Abstract

fetched live from OpenAlex

This study investigated the conditions under which discrimination can lead to social identity changes among members of a minority group. Both positive and negative relations between perceptions of discrimination and social identity have previously been reported. To explain the conflicting results and understand the complex reality of members of stigmatized groups, we argue that group-based emotions (e.g., group-based dissatisfaction) and ambiguity of discrimination cues (i.e., overt vs. ambiguous) need to be considered. We hypothesized that perceptions of discrimination would play a moderating role between group-based dissatisfaction and social identity change in a context of ambiguous, but not of overt, discrimination. The sample was comprised of 151 Arab Muslims living in the province of Quebec. Participants read fictitious newspaper articles portraying either overt (n = 76) or ambiguous (n = 75) discrimination towards in-group members. Results revealed that for participants in the overt discrimination condition, only group-based dissatisfaction was positively associated with social identity change. In contrast, for the participants in the ambiguous discrimination condition, those who perceived little discrimination and felt low group-based dissatisfaction reported a decrease in social identity. However, those who perceived low group discrimination and felt high group-based dissatisfaction reported a positive social identity change.

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)0.003
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 score0.900
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.454
Teacher spread0.369 · 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

Citations9
Published2016
Admission routes3
Has abstractyes

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