Bibliographic record
Abstract
Athletes frequently report using the motivational general-mastery function of imagery (e.g., overcoming challenging situations) and the motivational general-arousal function of imagery (i.e., managing emotions). Despite the reported frequency of using these imagery functions the relationship of imagery ability and the emotional experiences of athletes has received minimal attention in the literature. The purpose of the present study was to examine the relationship between athletes' motivational imagery ability and the emotional experience of those images. To assess the relationship between imagery ability and emotional experiences 43 undergraduate kinesiology students who regularly participate in sport completed the Motivational Imagery Ability Measure for Sport; requiring the imagery of 8 motivational imagery scenes rated on two 7 point scales: ease of image formation and strength of emotional experience. Participants also rated their emotional experience for the scene on the Sport Emotion Questionnaire (SEQ). Pearson correlations indicated athletes who had a better ability to experience emotions associated with both motivational general-mastery and arousal images experienced positive emotions (excitement and happiness) more intensely (r = .32-.55). Athletes who rated motivational general-mastery images as easy to form also rated excitement as higher (r = .45). No other relationships with the ease subscale and items on the SEQ were found.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".