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Record W2895115983 · doi:10.1017/s0954579418001128

Time-varying associations of racial discrimination and adjustment among Chinese-heritage adolescents in the United States and Canada

2018· article· en· W2895115983 on OpenAlexaffabout
Linda P. Juang, Yishan Shen, Catherine L. Costigan, Yang Hou

Bibliographic record

VenueDevelopment and Psychopathology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychologyClinical psychologyGerontologyMedicine

Abstract

fetched live from OpenAlex

The aim of our study was twofold: to examine (a) whether the link between racial discrimination and adjustment showed age-related changes across early to late adolescence for Chinese-heritage youth and (b) whether the age-related associations of the discrimination-adjustment link differed by gender, nativity, and geographical region. We pooled two independently collected longitudinal data sets in the United States and Canada (N = 498, ages 12-19 at Wave 1) and used time-varying effect modeling to show that discrimination is consistently associated with poorer adjustment across all ages. These associations were stronger at certain ages, but for males and females, first- and second-generation adolescents, and US and Canadian adolescents they differed. There were stronger relations between discrimination and adjustment in early adolescence for males compared to females, in middle adolescence for first-generation compared to second-generation adolescents, and in early adolescence for US adolescents compared to Canadian adolescents. In general, negative implications for adjustment associated with discrimination diminished across the span of adolescence for females, second-generation, and US and Canadian adolescents, but not for males or first-generation adolescents. The results show that the discrimination-adjustment link must be considered with regard to age, gender, nativity, and region, and that attention to discrimination in early adolescence may be especially important.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.647
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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