Naturalistic Observations of Spectator Behavior at Youth Hockey Games
Bibliographic record
Abstract
The purpose of the current study was to conduct an examination of spectator (i.e., parental) behavior at youth hockey games in a large Canadian city. Using naturalistic observation methods, an event sampling procedure was used to code spectators’ comments. Of specific interest were the type of remarks made, who made them (i.e., males versus females), the intensity of those remarks and whether they varied by child age, gender, and competitive level. We were also interested in whether the majority of onlookers’ comments were actually directed at the players, on-ice officials, or fellow spectators. Five observers attended 69 hockey games during the 2006–2007 hockey season. There was a significant variability in the number of comments made, with an average of 105 comments per game. The majority of the comments were generally positive ones, directed at the players. Negative comments, although quite infrequent, were directed largely at the referees. Females made more comments than did males, although males made more negative and corrective comments, and females made mostly positive comments. Comments varied significantly as a function of gender and competitive level. Proportionally more negative comments were made at competitive, as opposed to recreational games. An interaction was found for female spectators as their comments varied as a function of both the competitive level and the gender of the players. Results of this study are in direct contrast to media reports of extreme parental violence at youth hockey games, and provide unique information about the role of parental involvement at youth sporting events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".