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Record W2979571174 · doi:10.1111/bjso.12340

Participating in a new group and the identification processes: The quest for a positive social identity

2019· article· en· W2979571174 on OpenAlexaffabout
Diana Cárdenas, Roxane de la Sablonnière

Bibliographic record

VenueBritish Journal of Social Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyBasketballSocial psychologyIdentity (music)Social identity theoryValue (mathematics)Identification (biology)Group identificationCollective identitySocial groupImmigrationGroup (periodic table)GeographyPolitical science

Abstract

fetched live from OpenAlex

Immigrants experience identity shifts; they can identify with the new cultural group and, sometimes, identify less with their group of origin. Previous research suggests that participation in the new cultural group predicts these two identity shifts. However, these studies have exclusively used correlational methodologies. Furthermore, previous research ignored that when a group is negatively valued, individuals may not identify with it, even after participating in it, to preserve a positive social identity. This article tests with an experimental methodology whether participation recreated the identity shifts previously identified (greater identification with the new group and lower identification with the group of origin when perceiving dissimilarity). Furthermore, it tested how a group's value impacted these identity shifts following participation. Immigrants in Quebec (N = 184) either participated in Quebec's culture (watched hockey) or did not (watched basketball). Quebec's value was manipulated by changing whether Quebec won, tied, or lost the game. Compared to watching basketball, watching Quebec's team win or tie showed the hypothesized identity shifts, illustrating the importance of the new group's value when participating.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.405
Teacher spread0.370 · 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 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

Citations18
Published2019
Admission routes2
Has abstractyes

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