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Stigma, Social Identity Threat, and Health

2017· book-chapter· en· W2790571307 on OpenAlexaff
Brenda Major, Toni Schmader

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial identity theorySocial identity approachSocial psychologyIdentity (music)Situational ethicsPsychologyPsychological interventionStigma (botany)Identity formationSocial groupSelf-concept

Abstract

fetched live from OpenAlex

Abstract This chapter provides an overview of social identity threat theory and research and discusses its implications for health. The chapter defines social identity threat as the situationally triggered concern that one is at risk of being stigmatized and provides a conceptual model of its antecedents and consequences. Social identity threat stems from mere awareness of the cultural representations that associate a self-relevant social identity with undesirable characteristics, coupled with situational cues that bring these self-relevant cultural biases to mind, and personal characteristics that moderate one’s susceptibility to such experiences. Social identity threat can lead to involuntary psychological and physiological processes that when experienced repeatedly can have detrimental consequences for health. This chapter describes strategies that people use to cope with social identity threat and discusses their implications for health, in addition to providing a description of psychological interventions that can attenuate the negative effects of social identity threat.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.098
GPT teacher head0.340
Teacher spread0.242 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations42
Published2017
Admission routes1
Has abstractyes

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