Ceasing Participation in Leisure Activities After Immigration: Eastern Europeans and their Leisure Behavior
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".