Leisure Attitudes: A Follow-up Study Comparing Canadians, Chinese in Canada, and Mainland Chinese
Bibliographic record
Abstract
This study examines Mainland Chinese and Canadian's cognitive, affective, and behavioral leisure attitudes, and it compares these attitudes with those of Anglo-Canadians and Chinese in Canada (as reported in Deng, Walker, & Swinnerton; 2006). Data from 132 Mainland Chinese and 198 Canadians visiting, respectively, Tiantong Mountain National Forest Park and Elk Island National Park were obtained. Factor analysis of the Leisure Attitude Scale (Ragheb & Beard, 1982) resulted in four useable sub-scales: cognitive, affective, behavioral/preference, and behavioral/leisure education. A MANOVA and follow-up ANOVAs indicated that our study's Canadian participants had significantly (p < .01) higher mean scores than our Mainland Chinese participants on all but the behavioral/leisure education attitude scale. In order to compare our behavioral leisure attitude findings with Deng's et al., an averaged score was first calculated, and then t-tests of this and the other two leisure attitudes were conducted. Results indicated that: (a) our study's Mainland Chinese participants had a significantly (p < .01) lower cognitive leisure attitude mean score than Deng's et al. Chinese in Canada participants, and this finding held true irrespective of whether those in the latter group were low- or high-acculturated; and (b) our study's Canadian participants had significantly (p < .01) higher affective and behavioral leisure attitude mean scores than Deng's et al. Anglo-Canadian participants. The two studies' findings are compared and contrasted, and research recommendations are proposed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".