Manipulating athletes' perceptions of role ambiguity
Bibliographic record
Abstract
Role ambiguity refers to a lack of clear information associated with a group member's position (Kahn et al., 1964), and researchers have found that athletes are more satisfied and perceive their team as more cohesive when roles are clear (i.e., less ambiguous; Eys & Carron, 2001; Eys et al., 2003). However, most role ambiguity research has been descriptive and cross-sectional in nature. As such, researchers have emphasized the importance of examining role ambiguity using experimental methods (Beauchamp et al., 2002), though this requires a protocol for manipulating perceptions of one's role. One potential protocol takes advantage of the availability heuristic, which suggests individuals perceive events differently based on the ease by which they are recalled (Tversky & Kahneman, 1973). The purpose of the present study was to determine if athletes' perceived role ambiguity could be manipulated by the number of roles requested when asked to describe their contributions to their team. Participants included 112 (Mage = 20.20) male (n = 76) and female (n = 36) university/college/club athletes from interdependent team sports who were asked to provide descriptions of three or ten role responsibilities. Results were examined in light of starting status and interaction effects were found demonstrating that starters in the ten role condition expressed more role ambiguity than starters in the three role condition (p < .05; no differences were found for non-starters). Evidence of similar interaction effects among conditions regarding role satisfaction was found. Discussion is focused on theoretical implications of manipulating athletes' perceptions of role ambiguity.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".