The youth sport environment: A descriptive analysis
Bibliographic record
Abstract
To what extent does the phenomenon of poor behaviour occur in youth sport? Some experts posit that roughly 10% of athletes are exposed to unacceptable coaching behaviours within the youth sport context (David, 2005). However, few studies have attempted to quantify the extent to which poor behaviours occur by all youth sport participants (coaches, players, and spectators). The purpose of this study is to use data provided by an independent sport monitoring program, Justplay Behavioural Management Program (JBMP), to describe these behaviours. Through the JBMP officials are asked to rate the behavioural conduct of coaches, players, and spectators on a scale from 1 Very Good to 5 Very Poor. Ratings of 4 and 5 are known as critical incidents (CI; conduct deemed unacceptable by the official). This descriptive analysis examines multiple youth sports (hockey, baseball, football) over three seasons with regard to the extent of CI by all the participant groups. Analyses show that CI occurred in 40% of all hockey games, 37% of football games, and 36% of baseball games. Of these CI, coaches account for 42.6% (42.5% hockey; 34.7% baseball, 50.7% football), players for 26.7% (29.5% hockey; 31.1% baseball, 19.5% football), and spectators for 30.2% (26.7% hockey; 34.2% baseball, 29.6% football). These numbers indicate behaviours, which may have long-term negative effects on participants, occur to a worrisome degreeAcknowledgments: 1. David, P., Human Rights in Youth Sports: A Critical Review of Children's Rights in Competitive Sports, Routledge, New York, 2005.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".