The assessment of subgroups in youth sport via interviews informed by social network analysis
Bibliographic record
Abstract
Despite recent qualitative research investigating the nature of subgroups in sport (Martin et al., 2015, 2016), several issues remain. Notably, these studies were conducted with adult samples, and did not consider the social position of the athletes. As such, the current study sought to explore subgroups in a youth sport setting, with a specific mandate to elicit perceptions from athletes who represented various social positions within a team. Following a two-phase process, athletes (N = 39; Mage = 16.05, SD = 0.86) from three youth hockey teams completed questionnaires to identify the social networks of friendships reported by team members. These data were subsequently analyzed using UCINET (Borgatti et al., 2002) to identify included and excluded athletes based on centrality scores. Semi-structured interviews were conducted with four included and four excluded athletes (Mage = 17, SD = 0.76; 2 females) to explore their perceptions of the subgroups within their current team. As a general summary of our results, subgroups were described as inevitable, variable, and identifiable sub-entities that formed within teams, and all participants discussed positive and negative outcomes emanating from their presence. Interestingly, the majority of negative discussions involved the term 'cliques.' The athletes also advanced a number of factors that contributed to subgroup or clique development (e.g., school enrolment, skill level), and highlighted certain contextual issues (e.g., competitive level of team/league, school vs club sport, age of athletes) that should be considered. Finally, suggestions and current practice for avoiding or manipulating problematic cliques were advanced.Acknowledgments: Funding provided by the first authors SSHRC Insight Development Grant (430-2014-00353)
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".