Identifying components of athletes' subgroup perceptions: A conceptual and qualitative approach
Bibliographic record
Abstract
Recent advances in team dynamics research suggest subgroups to be an inevitability in sport, and that their presence can have facilitative and debilitative implications at both athlete and team levels (Wagstaff & Martin, 2018). Interestingly, the relative impact that subgroups have within a team appears to revolve less around their objective presence, and more so around athletes' subjective experiences (Martin et al., 2015). As such, the purpose of this study was to advance a series of components expected to be indicative of the way that athletes perceive subgroups in sport. Using a critical realist approach, a theory-driven literature review informed a preliminary list of perceptual components that were then assessed in detail through face-to-face or virtual focus-group interviews with 28 athletes (61% female; Mage = 22.2) from a range of sports (e.g., basketball, hockey, rowing). Audio recordings were transcribed verbatim, coded to identify demi-regularities, and analyzed through abduction and retroduction. The resulting proposed subgroup components include: Observability (e.g., distinctiveness), structural (e.g., variability, exclusivity), organizational orientation (e.g., citizenship behavioural tendencies), organizational representativeness (e.g., team level prototypicality of subgroup members), and affective (e.g., feelings associated with particular subgroups). These components uncover numerous ways that subgroups may be observed by athletes and could further inform our understanding of athletic experiences in sport. Indeed, whereas the traditional discourse pertaining to subgroups has involved trepidation and avoidance, the proposed components can provide a foundation for a more nuanced approach to their investigation.Acknowledgments: This research project was funded by the Social Sciences and Humanities Research Council of Canada, Canada Graduate Scholarship for Master's Students.
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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".