Similarities and differences in constraints and constraint negotiation among Japanese sport tourists: a case of masters games participants
Bibliographic record
Abstract
The purposes of this study were to examine (a) similarities and differences in constraints to and constraint negotiation for masters games (MG) participation, and (b) the relationships among constraints, constraint negotiation, and intention to participate in the World Masters Games 2021 Kansai, across international sport tourists, domestic sport tourists, and sport excursionists. An online survey was conducted with 449 Japanese people who participated in MGs within the last three years. Our results indicated that international sport tourists experienced higher levels of psychological, physiological, interpersonal, financial, tourism, commitment, MG-specific constraints than domestic sport tourists and sport excursionists, although physiological constraints did not differ between international and domestic sport tourists. Conversely, international and domestic sport tourists utilized tourism and MG-self-adaptation negotiation strategies more than sport excursionists. Lastly, constraints to and constraint negotiation for the past MG were not related to the intention of participation in the World Masters Games 2021 Kansai across the three groups. These results suggest that this typology of sport tourists – international, domestic, and excursionist – is an effective framework to understand constraints and negotiation as well as other behaviors and experiences of Japanese sport tourists.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".