The Effects of Culture on Conceptualizations of Leisure, Control, and Positive Affect Between Japanese and Canadian Undergraduate Students
Bibliographic record
Abstract
The purpose of this dissertation is to examine: (a) similarities and differences in conceptualizations of leisure between Japan and Canada and between two Japanese leisure-like terms: yoka and rejā (Study 1); (b) the effects of leisure participation on Japanese and Canadian undergraduate students’ control and positive affect (Study 2); and (c) self-construal’s moderator effects on leisure experiences (Study 3). Results of Study 1 indicated that: (a) conceptualizations of leisure differed not only between Japan and Canada but also within Japan depending on terminologies; (b) the loanword rejā has different connotative meanings from its original English word, leisure, suggesting that it has adapted to Japanese cultural contexts; and (c) the Japanese leisure-like term that best compares with the English word leisure varies depending on which specific aspect of leisure is of interest. Results of Study 2 indicated that leisure participation significantly: (a) increased Japanese students’ primary control (changing the surrounding activity/event); (b) decreased the acceptance aspect of secondary control (accepting the surrounding activity/event) for Japanese and Asian- and Euro-Canadian students; (c) increased and decreased, respectively, the adjustment aspect of secondary control (adjusting oneself to the surrounding activity/event) for Japanese and Euro-Canadian students; and (d) increased high- and low-arousal positive affect for Japanese and Asian- and Euro-Canadian students, but the positive effects on high-arousal positive affect for Japanese students were significantly larger than those for Euro-Canadian students. Results of Study 3 indicated that the moderator effects of self-construal do not appear to be a key mechanism that explains cultural differences in leisure experiences. In conclusion, this dissertation identified both universality (similarities) and cultural specificity (differences) in conceptualizations of leisure and leisure experiences, and indicated that self-construal is not a panacea to explain why cultural differences in leisure phenomena exist. This dissertation contributes to the advancement of the collective body of knowledge in leisure studies by conducting cross-cultural research between the West (Canada) and the non-West (Japan), by developing a new method (Leisure Ten Statements Test) to examine conceptualizations of leisure, and by providing another piece of evidence that leisure participation is conducive to psychological well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".