The Event‐Tourist Career Trajectory: A Study of High‐Involvement Amateur Distance Runners
Bibliographic record
Abstract
Drawing from theory on serious leisure, social worlds, recreation specialization, ego‐involvement, and travel motivation, it is proposed that many people with specific sport or lifestyle interests will develop event‐specific careers. These careers will follow a trajectory that can be measured in terms of six dimensions: motivations (especially the pursuit of higher‐level personal needs); changing travel styles; spatial and temporal patterns, event and destination choices. As a partial test of the event‐tourist career trajectory, a large sample of registrants for a half‐marathon in Sweden was questioned in a pre‐event survey about their motives, involvement in their sport, and event‐related travel. Employing an involvement scale specific to amateur distance runners, analysis revealed that most runners were not highly‐involved in this sport. However, a comparison of the most highly‐involved (constituting the top decile of the sample), and the remainder, revealed many significant differences that do support the hypotheses in all six dimensions. Implications are drawn for theory development and future research, as well as for the design and marketing of sport events aimed at niche market segments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".