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Ceasing Participation in Leisure Activities After Immigration: Eastern Europeans and their Leisure Behavior

2002· article· en· W2092207239 on OpenAlexvenueaboutno aff
Monika Stodolska

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2002
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationLeisure timeLeisure activityDemographic economicsSociology of leisureLeisure studiesSociologyPolitical sciencePsychologyEconomicsTourismSocial psychologyPhysical activitySocial scienceMedicine

Abstract

fetched live from OpenAlex

Although leisure of ethnic and racial minorities has attracted significant attention from leisure scientists, research on leisure behavior of immigrants is only developing and gaining recognition as a legitimate area of inquiry. This paper examines the role of leisure in immigrants’ adaptation by focusing on one aspect of the process of post-arrival leisure change – ceasing participation in leisure activities after settling in the host country. Based on 13 in-depth interviews and a questionnaire survey completed by 264 recent Polish immigrants to Edmonton, Alberta, it explores in-depth ceasing participation patterns among immigrant populations. The study identifies major reasons immigrants have for ceasing participation in their former pastimes (lack of time, environmental differences and financial difficulties), groups of activities most commonly ceased by the newcomers (outdoor recreational activities, typical Polish activities and home-based recreation) and explains the reasons that Eastern European immigrants have for abandoning participation in their favorite activities. Findings of this study are subsequently used to isolate patterns of changes in post-arrival leisure behavior that are likely to be universally applicable as well as to establish factors that might differentiate specific immigrant populations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.321
Teacher spread0.285 · 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.

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

Citations16
Published2002
Admission routes2
Has abstractyes

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