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Record W1955788977 · doi:10.1348/014466610x519683

Facing social identity change: Interactive effects of current and projected collective identification on expectations regarding future self‐esteem and psychological well‐being

2010· article· en· W1955788977 on OpenAlexaff
Dominic J. Packer, Alison L. Chasteen, Sonia K. Kang

Bibliographic record

VenueBritish Journal of Social Psychology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyValence (chemistry)Social identity theorySalientSocial psychologyYoung adultSelf-esteemIdentification (biology)Developmental psychologyIdentity (music)Ingroups and outgroupsSocial group

Abstract

fetched live from OpenAlex

We hypothesized that prospective changes in social identity that involve transitioning out of a currently valued group would be associated with negative expectations regarding future states, but that this effect would be mitigated among individuals who expect to belong to a future in-group of similar importance. Consistent with predictions, strongly identified young adults in two studies projected significantly lower self-esteem/psychological well-being in old age than weakly identified young adults. Critically, however, this effect was fully attenuated if they expected to identify with their future aged in-group when they were old. Study 2 showed that the capacity for projected identification to buffer projected well-being among strongly identified young adults was contingent on their membership in the future in-group being highly salient. Analyses of participants' written descriptions of old age (Study 1) and a valence manipulation (Study 2) indicated that these effects were not attributable to the anticipated valence of future selves/states, but rather to the value placed on current and future group memberships.

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 score0.938
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.410
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; 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

Citations7
Published2010
Admission routes1
Has abstractyes

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