Bibliographic record
Abstract
<!-- /* Font Definitions */ @font-face {font-family:"Times New Roman"; panose-1:0 2 2 6 3 5 4 5 2 3; mso-font-charset:0; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:50331648 0 0 0 1 0;} @font-face {font-family:Arial; panose-1:0 2 11 6 4 2 2 2 2 2; mso-font-charset:0; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:50331648 0 0 0 1 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0in; margin-bottom:.0001pt; text-align:justify; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Arial;} table.MsoNormalTable {mso-style-parent:""; font-size:10.0pt; font-family:"Times New Roman";} @page Section1 {size:8.5in 11.0in; margin:1.0in 1.25in 1.0in 1.25in; mso-header-margin:.5in; mso-footer-margin:.5in; mso-paper-source:0;} div.Section1 {page:Section1;} --> Research indicates that the culture of sport tends to promote mythologized perceptions of the benefits of participation for youth (Giulianotti, 2004). However, a growing body of literature suggests (e.g., Brackenridge 2007; David 2005, Author 2010a) that abusive coaching behaviours may be prevalent in the youth sport system. Estimation of the problem and resultant policy development has been hindered by the lack of monitoring of these behaviours in these contexts. It is the purpose of this study to examine both (a) the extent of negative coaching behaviours in four youth sports; and (b) the utility of an online system of behavioural management (i.e., monitoring). For this study three independent raters collectively identified 256 behaviours from 164 comments made by officials in four different sports (ice hockey, baseball, football, and basketball). The behaviours were then deductively analyzed and categorized using the Typology of Coaching Transgressions (TOCT) model (Author et al. 2010a). The original data were collected by the Justplay Behaviour Management Program (JPBMP). Results indicate that across all sports 77% of all harmful coaching behaviours are indirect in nature. Additionally, across all sports the psychological (38%) and modeling categories (27%) contain the greatest percentage of harmful behaviours. The two contact sports contained the greatest number of direct and neglectful abuses. Discussion revolves around the importance of providing data for the development of policies regarding coaching behaviours and also the utility of the TOCT model in the identification of negative coaching behaviours.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".