Ethnic enclosure, social networks, and leisure behaviour of immigrants from Korea, Mexico, and Poland
Bibliographic record
Abstract
The goal of this study was to provide an understanding of the concept of ethnic enclosure in leisure and the effect it has on the lives of immigrants after their settlement in the host country. This study explored the reasons that motivate ethnic minorities to associate predominantly with members of the same ethnic group and determined possible consequences of ethnic enclosure in leisure. Analysis presented in this study is based on semi‐structured, in‐depth interviews that were conducted in Chicago, Illinois, between April and October 2001 with 39 first‐generation immigrants from Korea, Mexico, and Poland. The overwhelming majority of interviewees confirmed that members of their own ethnic group constituted their primary leisure companions. Commonly mentioned explanations for the ethnic enclosure in leisure included comfort level, similar experiences, common culture, lack of conversation topics with mainstream Americans, lack of English language skills, discrimination/exclusion by the mainstream, and fear of the unknown. Limiting leisure contacts to members of their own ethnic group provided psychological and emotional comfort to immigrants as well as certain tangible economic benefits. On the other hand, it delayed their assimilation, led to difficulties in securing employment, and hindered advancement in the workplace.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".