A Study of Nonfans and Fans of the National Lacrosse League's Edmonton Rush
Bibliographic record
Abstract
The National Lacrosse League (NLL) is floundering. In an attempt to understand why NLL fans attend games and other sport fans do not, the NLL’s Edmonton Rush were studied. To best address the NLL’s attendance woes, two primary research questions were developed: 1) Why do fans of the Oilers and Oil Kings choose not to attend Edmonton Rush games? 2) Why do fans of the Edmonton Rush attend games? To answer these questions an online focus group along with a document analysis of Rush media, and a telephone interview were used to collect data. \nThe data collection methods mentioned above assisted in answering the primary and secondary research questions, which allowed three major themes along with sub-themes to inductively emerge. The nonfans of the Rush do not attend Rush games because of the connection they have with hockey and the disconnection they have with lacrosse, some are simply not interested or were not entertained, as well as the lack of exposure the Rush receive. The Rush fan participants attend Rush games because of Edmonton community pride, the entertainment value they get out of attending a game, it is a great alternative new sport experience and it either is a substitute or a compliment to hockey. Both the nonfan and fan participants of this study believe that different marketing approaches can be utilized in order to attract nonfans to attend games.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".