The Impact of Partner Performance on Emotions in Doubles Racquet Sports
Bibliographic record
Abstract
Purpose: Different athletes may experience different emotions, based on one’s appraisal of the situation or environment. To date, this line of research has received limited research attention in sport dyads. The purpose of this study was to understand the role of a partner’s play on the different types of emotion of athletes, as well as perceived impact on overall emotions, performance, and motivation in doubles racquet sports (i.e., tennis, badminton, and squash). Method: Using a post-test only, randomized experimental design, participants read one of three possible written vignettes that depicted different scenarios of their partner’s play (i.e., poor, good, or usual performance). Participants (N = 103) were then asked to fill out a questionnaire packet based on the scenario read. Results: A Multivariate Analysis of Variance, as well as follow-up Analyses of Variance revealed significant differences between groups in subjective emotions based on whether their partner was playing poorly, their partner having a good performance or whether their partner was perceived as playing their usual game (control group). The results showed that athletes scored higher in anger and anxiety when their partner is playing poorly, and when their partner is having a good performance, athletes had higher scores in happiness and excitement. Conclusion: Overall, these findings imply that athletes’ emotions may change based on how their partner is playing and provide a foundation to look at emotional reactions (action tendencies) and coping in sport teams.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".