Predictive power of leisure constraint-negotiation models within the leisure-time physical activity context: A partial least squares structural equation modeling approach
Bibliographic record
Abstract
Although prediction is a stated goal of leisure constraints and constraint negotiation research, to date, empirical studies have solely focused on statistical explanation when comparing competing models. Our research, therefore, examined the predictive power of five leisure constraint-negotiation models within the context of leisure-time physical activity (LTPA). To do so, we utilized online survey data from 299 Japanese and 286 Euro-Canadian adults and employed partial least squares equation modeling’s (PLS-SEM) predictive function. Our results indicated that the independence model—in which constraints, negotiation, and motivation directly and separately predict participation—demonstrated better predictive power than the four alternatives. This finding was consistent across strenuous, moderate, and mild levels of LTPA and between the two national groups. We discuss the theoretical, practical, and methodological implications of our findings, and call for future research and theory development based on both explanatory and predictive evidence.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".