How Pre-Event Engagement and Event Type Are Associated with Spectators’ Cognitive Processing at Two Swimming Events
Bibliographic record
Abstract
Little is known about event experiences of spectators attending non-mainstream sports, let alone different types of events within the same sport. Past research has demonstrated that pre-event engagement influences event experiences. Therefore, the purpose of this study was to examine the ways in which pre-event engagement and event type were associated with spectators’ event experiences at two different types of swimming events. Surveys were collected from 90 spectators attending the 2016 National Swimming Championships (NSC) in Belgium, and from 246 spectators attending the 2016 World Swimming Championships (WSC) in Canada. Pre-event engagement included behavioral measures, as well as cognitive and affective measures. Event experiences were measured with Madrigal’s (2006 Madrigal, R. (2006). Measuring the multidimensional nature of sporting event performance consumption. Journal of Leisure Research, 38(3), 267–292. https://doi.org/10.1080/00222216.2006.11950079[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) FANDIM scale. A series of regression analyses were used to explore if and how spectators’ pre-event engagement and event type were associated with their event experiences, while controlling for socio-demographic variables. Knowledge of and interest in the sport, as well as awareness of opportunities to participate were associated with aesthetics, evaluation, and flow. Pre-event engagement in terms of prior spectating and active participation were not associated with event experiences. The type of event matters and the WSC generated more engaged event experiences (i.e., aesthetics and fantasy). To improve event experiences at non-mainstream events, managers should seek to implement initiatives to increase interest among potential spectators, including those with little or no behavioral engagement with the sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".