Examining the relationship between collective efficacy and team explanatory style
Bibliographic record
Abstract
Within the sport realm, collective efficacy is considered an important construct that influences all aspects of team behaviour (Bandura, 1986). As such, a considerable amount of research has examined the relationship between collective efficacy and behavioural (e.g., performance), cognitive (e.g., team goals), and affective (e.g., cognitive anxiety) outcomes. The purpose of the present study was to extend previous research to determine the relationship between team explanatory style (the cognitive predisposition to explain the causes of bad events in a habitual manner) and collective efficacy beliefs. Athletes (n = 148; 14 teams) completed the Team Attributional Style Questionnaire (TASQ; Shapcott & Carron, 2010) and the Collective Efficacy Questionnaire for Sports (CEQS; Short, Sullivan, & Feltz, 2005). Statistical analyses supported the aggregation of individual athlete responses to represent a group-level construct for both team attributional style and collective efficacy beliefs. Partial least squares (PLS) structural modeling technique showed that the four factor attributional style model (i.e., controllability, universality, globality, and stability) accounted for 22% of the variation of the global measure of collective efficacy. The findings will be discussed in terms of their implications for the dynamics of sport teams. Acknowledgments: Funded by SSHRC
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".