An observational assessment of athletes' engagement and social interactions in an english football academy: The Revised Athlete Behaviour Coding System (R-ABCS) case study
Bibliographic record
Abstract
Athletes' social interactions represent an important context through which positive development can be fostered (Fraser-Thomas & CA´tA©, 2009). However, the existing literature has relied primarily on the use of retrospective or self-report measures that may not fully capture the complexity of athletes' behaviours (Meredith, Dicks, Noel, & Wagstaff, 2018). As such, there is a need to use observational techniques to explore what athlete behaviours look like in naturalistic settings (Smith, 2003). Thus, the purpose of this case study was to use observational methods to understand athletes' engagement in activities and social interactions within the context of an English football academy. Ten athletes in an under-11 academy were observed over seven practices. Practices were purposefully sampled to represent a diverse range of contexts (e.g., practice activities, scrimmages, and team meetings). Athletes' behaviours were coded using an adapted version of the Athlete Behaviour Coding System, which assesses behaviours according to their content, target, quality, and the context in which they occur (Vierimaa & CA´tA©, 2016). Findings indicated that athletes most commonly exhibited communicative behaviours (M = 359.54 counts/hour, SD = 182.40 counts/hour), followed by engaged (M = 236.31 counts/hour, SD = 223.56 counts/hour) and non-cooperative behaviours (M = 1.38 counts/hour, SD = 5.12 counts/hour). It was also found that athletes touched the ball more during practice activities (M = 99.17 counts/hour, SD = 54.97 counts/hour) than during scrimmages (M = 90 counts/hour, SD = 60 counts/hour). The theoretical and practical implications of this research for coaches and sport practitioners will be discussed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".