Culture, Leisure Interpretation, and Ideal Affect during Leisure: A Situation Sampling Approach
Bibliographic record
Abstract
Definitions of leisure and emotional experiences during leisure vary across cultures, but have been understudied. To elucidate the relationships between leisure, emotion, and culture, we adopted a cultural psychology method called situation sampling. Using an onsite survey, we collected leisure and non-leisure situations from 126 Euro-Canadian and 149 Mainland Chinese undergraduate students. Employing an online survey, we then asked another 203 Euro-Canadian and 228 Mainland Chinese undergraduate students about their interpretation of, and ideal positive affect within, randomly sampled situations. Although both groups distinguished leisure from non-leisure situations regardless of culture, results of the mixed ANOVA indicated Euro-Canadians interpreted Canadian leisure situations as leisure more highly than Mainland Chinese did. Moreover, Chinese leisure situations were more conducive to positive engaging emotions (e.g., friendly) than Canadian leisure situations, and Chinese participants idealized this kind of affect in leisure situations more than their Euro-Canadian counterparts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".