Exploring the relationship between sociometric status and peer interactive behaviour in youth sport
Bibliographic record
Abstract
In 2003, Smith suggested in an influential review paper that behavioural observation and sociometry were two potentially useful but under-utilized methods for the study of peers in youth sport. Despite this call, the methods used to study peers in sport remain largely focused on athletes' perceptions through questionnaires and interviews (Murphy-Mills, Bruner, Erickson, & Côté, 2011). Thus, the purpose of this project was to examine sociometric status, sport competence, and peer interactive behaviour in a youth sport context using an observational coding system. Female volleyball players (N = 29; Mage = 16; SD = 1.39) from three competitive teams completed the sport competence and peer connection inventories (Vierimaa, Erickson, Côté, & Gilbert, in press), and each team was videotaped during three practices. An observational coding system comprised of seven categories was developed and used to code athlete behaviours in a continuous, time-based manner and this data was compared across teams and sociometric status groups. Consistent with past research, popular athletes received significantly higher (p < .05) peer ratings of sport competence. Behavioural profiles were constructed for each sociometric status group, which revealed behavioural variation between groups in multiple categories (e.g., prosocial behaviour and overall sociability). Results will be presented in greater detail and implications for future research and practice will be discussed.Acknowledgments: This research was supported by the Social Sciences and Humanities Research Council.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".