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Record W2169011963 · doi:10.1037/0022-3514.85.6.1147

How Are Social Identities Linked to Self-Conception and Intergroup Orientation? The Moderating Effect of Implicit Theories.

2003· article· en· W2169011963 on OpenAlexaff
Ying‐yi Hong, Gloria Chan, Chi‐yue Chiu, Rosanna Y. M. Wong, Ian Hansen, Sau-lai Lee, Yuk‐Yue Tong, Jeanne Ho‐Ying Fu

Bibliographic record

VenueJournal of Personality and Social Psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologySocial psychologySocial identity theoryIdentification (biology)Meaning (existential)Character (mathematics)Identity (music)Social identity approachBiology and political orientationContext (archaeology)Social environmentOrientation (vector space)Social groupIngroups and outgroupsPoliticsSociology

Abstract

fetched live from OpenAlex

Social identity approaches assume that social identification affects both self-conception and intergroup orientation. The authors contend that such social identification effects are accentuated when people hold a fixed view of human character and attribute immutable dispositions to social groups. To these individuals, social identities are immutable, concrete entities capable of guiding self-conception and intergroup orientation. Social identification effects are attenuated when people hold a malleable view of human character and thus do not view social identities as fixed, concrete entities. The authors tested and found support for this contention in three studies that were conducted in the context of the Hong Kong 1997 political transition, and discussed the findings in terms of their implications for self-conceptions and the meaning of social identification.

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)0.009
metaresearch head score (Gemma)0.054
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 score0.012
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.000

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 head0.027
GPT teacher head0.373
Teacher spread0.346 · 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

Citations64
Published2003
Admission routes1
Has abstractyes

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