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Effects of Acculturation, Leisure Benefits, and Leisure Constraints on Acculturative Stress And Self-esteem Among Korean Immigrants

2005· article· en· W2063700206 on OpenAlexvenueno aff
David Scott, Chi‐Ok Oh

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2005
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationPsychologySelf-esteemConstruct (python library)ImmigrationStress (linguistics)Social psychologyGeography

Abstract

fetched live from OpenAlex

In this study, we sought to assess the role that leisure benefits and constraints play in facilitating acculturation, allaying acculturative stress, and enhancing self-esteem among Korean-Americans. Data were collected from a sample of Koreans living primarily in large cities in the United States. Results provided partial support for the proposed theoretical model. As predicted, acculturation was negatively related to acculturative stress and positively related to self-esteem, and acculturative stress was negatively related to self-esteem. The leisure benefits construct was significantly and positively related to only self-esteem. Contrary to what we predicted, however, the leisure benefits construct was not significantly related to acculturation and was positively, rather than negatively, related to acculturative stress (although only at the 0.1 level of significance). Finally, the leisure constraints construct was significantly related to acculturative stress but not to the level of acculturation or self-esteem.

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 score0.003
Threshold uncertainty score0.006

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.014
GPT teacher head0.293
Teacher spread0.279 · 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

Citations27
Published2005
Admission routes1
Has abstractyes

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