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Record W2898583683 · doi:10.1111/pops.12530

Solidarity Not Homogeneity: Constructing a Superordinate Aboriginal Identity That Protects Subgroup Identities

2018· article· en· W2898583683 on OpenAlexafffund
Scott D. Neufeld, Michael T. Schmitt

Bibliographic record

VenuePolitical Psychology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSuperordinate goalsMulticulturalismSocial psychologyPsychologyIdentity (music)SociologySocial identity theoryGender studiesSocial group

Abstract

fetched live from OpenAlex

Superordinate identities formed around shared oppression provide political and psychological resources for marginalized groups. However, superordinate identities can also threaten the identities of the subgroups they attempt to bring together. We examined how a superordinate identity was constructed to protect subgroup identities using data from 31 urban Aboriginal participants who strongly identified with both their subgroup (heritage cultures) and superordinate Aboriginal identities. Participants defined the superordinate Aboriginal identity as a fundamentally diverse category where no one subgroup was more representative of the wider category than others. Participants also put their respect for subgroup diversity into practice by regularly engaging with Aboriginal (subgroup) cultures other than their own. Finally, participants felt that representations of the superordinate Aboriginal category should prioritize local cultures. We discuss these findings in relation to research in social psychology on superordinate and subgroup identities, multiculturalism, and collective resistance and provide some suggestions for how this work may be extended.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

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 studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.074
GPT teacher head0.446
Teacher spread0.372 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations14
Published2018
Admission routes2
Has abstractyes

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