How Visitors and Locals at a Sport Event Differ in Motives and Identity
Bibliographic record
Abstract
Although the literature on events differentiates between locals, casual attendees, and those who have travelled specifically to attend the event, little is known about how the types of attendee differ. This study compared the fan motivation, leisure motivation, and identification with the subculture of athletics reported by a sample (N = 777) of attendees at the 2005 Pan American Junior Athletics Championships. Age, gender, and income were also included. Regression analyses were used to determine the structure of relations among the variables, and to ascertain whether the levels of motivation or identity varied among the three types of attendee. Tests for linear restrictions were used to determine whether the structure of relations among the variables differed by type of attendee. The structure of relations among the variables did not differ among the three types of attendee, but attendees who had travelled specifically to attend the event reported substantially higher identification with the subculture of athletics, and slightly higher fan motivation. Identification with the subculture of athletics mediated much of the effect. Females reported higher fan motivation and higher leisure motivation than did males. Age had a small but significant relationship with fan motivation, and income had a small but significant relationship with leisure motivation. Findings are generally consistent with predictions derived from theories of motivation, subculture, and gender roles. It is suggested that marketing communications directed out-of-town should highlight opportunities to strengthen, parade, and celebrate, while those in the local trading radius should underscore the entertainment, aesthetics, and vicarious achievement featured at the event.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".