The development and salience of role responsibilities in sport teams
Bibliographic record
Abstract
Athletes' perceptions of the development and salience of role responsibilities were explored through two projects. First, interviews were conducted with male (n = 8) and female (n = 7) athletes to understand the types of roles that they occupy, and how the expectations for these roles developed. Four general role types emerged: specialized-task roles (e.g., rebounder), auxiliary-task roles (e.g., encourager), leadership roles, and social roles. Specialized-task roles were typically prescribed by a coach, while auxiliary-task and social roles tended to evolve out of group interactions. In the second project, 237 athletes were asked to identify the roles that they perceived to hold. These roles were able to be categorized according to the four role types identified in the first project. Chi-square analyses were conducted to explore the distribution of roles across various sub-groups. Odds ratios indicated that starters were 5.57 times more likely to identify specialized task and leadership roles, while non-starters were 3.93 times more likely to identify auxiliary task roles, all ?2 (1, N = 237) > 15.54, p < .001. The predominance of non-starters who identified auxiliary-task roles suggests a greater reliance on the tacit transfer of role information and the tenets of interpersonal sensemaking explain the salience of various role types in relation to starting status. Implications pertaining to theories of organizational socialization and measurement are 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".