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Record W4391050518 · doi:10.1177/00110000231225473

Cultural Stressors and Cultural Identity Styles Among Hispanic College Students

2024· article· en· W4391050518 on OpenAlexaff
Beyhan Ertanir, Colleen Ward, Sofía Puente-Durán, Cory L. Cobb, Alan Meca, María Fernanda García, Ágnes Szabó, Jaimee Stuart, Christopher P. Salas‐Wright, Miguel Ángel Cano, Jennifer B. Unger, Aigerim Alpysbekova, Seth J. Schwartz

Bibliographic record

VenueThe Counseling Psychologist · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsStressorPsychologyCultural identityPath analysis (statistics)Social psychologyIdentity (music)Style (visual arts)Context (archaeology)Developmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

Research shows that cultural identity styles (CIS; i.e., hybrid identity style [HIS] vs. alternating identity style [AIS]) and cultural stressors (i.e., discrimination, negative context of reception, and bicultural stressors) are associated, but the directionality of this association remains unclear. Using a 2-wave, self-report dataset and a cross-lagged design, we examined the directionality of the associations between cultural stressors and CIS among 824 first- and second-generation U.S. Hispanic college students over a 12-day period. Across two waves, results of our path analysis indicated that in particular CIS temporally predict cultural stressors rather than vice versa. Whereas AIS predicted higher levels of perceived cultural stressors, HIS predicted lower levels of perceived cultural stressors. Moreover, contrary to our expectations, we also found a small negative effect of perceived discrimination on AIS. These findings suggest that HIS may play a more favorable role than AIS for bicultural identity formation and for decreasing cultural stressors.

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)0.002
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.458
Teacher spread0.394 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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