An examination of team structure and its implications for subgroups in an individual sport setting
Bibliographic record
Abstract
Despite growing research interest in the social dynamics of sport teams, the smaller groups that emerge from within a total team have largely been overlooked. This is concerning given that preliminary investigations highlight the inevitability of their presence, in addition to the potential for them to generate both adaptive and maladaptive outcomes (Martin et al., 2015, 2016; Wagstaff et al., 2017). However, several shortcomings identified in this exploratory body of literature were its (a) inclusion of only one perspective (either athletes or coaches), (b) from a range of different sport teams, without consideration for (c) changes over time, and (d) the specific sport structure that might predispose division within a team. As such, the purpose of this study was to explore how sport structure predisposes a team to subgroup and clique formation, and thus, influences athlete interactions and team functioning. Track and field was chosen as it represents an individual sport composed of several smaller teams ranging in teammate interdependence and event types. An in-depth case-study design was selected, composed of several interviews conducted over the course of a competition season. A single Canadian intercollegiate track and field team was selected, wherein 15 participants (4 coaches, 11 athletes) provided various perspectives from across the team. Semi-structured interviews were conducted early and post season, and transcripts were analyzed using a combination of thematic and exception analyses (Braun et al., 2016; Phoenix & Orr, 2017). Results indicated that this track and field team had a multitude of inherent structural constraints (sport type, event type, facility/schedule limitations, team size, change over time) that directly impacted athlete interactions and predisposed the group to subgroup and clique formation. Consequently, much of the team’s energy was directed toward the management of the social environment (e.g., athlete interactions, cooperation) by undertaking team building activities, utilizing the leadership core, emphasizing consistent and quality communication, and generating individual buy-in. These findings have both theoretical and practical implications and are discussed as they related to the literature. Future directions and study strengths and limitations are advanced herein.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".