Correcting Player Mistakes: Effects of Coach and Player Social Influence on Increasing Player Intention to Intervene with Teammates
Bibliographic record
Abstract
Correcting mistakes is a key component in sport performance. Typically, the coach is tasked with providing this feedback. While teammates could serve this role, it is not the norm for teammates to provide this feedback (Goldsmith & Fitch, 1997). The purpose of the present study was to examine the effect of coach and player sources of social influence on increasing player intention to intervene with teammates following a technical mistake. Adult soccer players (N = 170) read one of five hypothetical soccer team vignettes where the description differed in the levels of coach and player social influence about intervening with teammates. Participants rated their intention to intervene with teammates who made a technical mistake during a game. ANCOVA results indicated that the overall model was significant (p < .002). Post hoc analyses revealed that intention to intervene was higher when teams were described as having a coach that encouraged players to intervene and the team norm was for players to intervene. However, this was not the case when coach and teammate social influences were at cross purposes. This provides initial support that aligned social influences from the coach and players increase soccer players’ intentions to intervene when their teammates make technical mistakes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".