Subcultural identification and motivation of spectators at the 2005 Pan American Junior Athletics Championships.
Bibliographic record
Abstract
Due to the high costs associated with hosting major sporting events it is necessary from an event organizer's perspective to design marketing strategies that aim to maximize the number of spectators in attendance. Previous research has shown that identification with the sport subculture, leisure and fan motives act as strong predictors of attendance at sporting events. The purpose of the study was to examine the existence of and relationship between these constructs at a one-time medium sized track and field event. Results indicate a number of valuable preliminary theoretical and practical insights into the understanding of sport consumer motives at special events. The key findings that are unique to special events and warrant future research include the strong relationship between identification with the sport subculture and motivation, the possible presence of a gender threshold effect, and the influence of a host destination's sport history and tradition. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .S64. Source: Masters Abstracts International, Volume: 45-01, page: 0116. Thesis (M.H.K.)--University of Windsor (Canada), 2006.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".