Assessing what generates precompetitive emotions: development of the precompetitive appraisal measure
Bibliographic record
Abstract
Athletes' precompetitive appraisal determines which emotion they experience with regard to an upcoming competition. Such precompetitive emotions have powerful and potentially destructive consequences for performance. To control and optimise these consequences, it is important to examine precompetitive appraisal. Currently, such efforts are hampered by the lack of a valid measurement tool. The present study aimed to develop a novel measure of precompetitive appraisal. Specifically, the Precompetitive Appraisal Measure (PAM) was constructed by adapting an existing self-report scale. Female and male intercollegiate team sport athletes (N = 384) completed the PAM, along with a measure of intensity and interpretation of precompetitive anxiety symptoms (CSAI-2D) prior to competition. On these responses, (a) a Principal Component Analysis and a Confirmatory Factor Analysis supported the PAM's suggested two-factor structure (Primary and Secondary Appraisal), (b) cluster analyses indicated the measure's ability to distinguish theoretically congruent appraisal profiles (Threat and Challenge) and (c) a MANOVA and multiple regression analyses demonstrated that PAM-responses predicted precompetitive symptom intensity and interpretation. Further, analyses revealed that the majority of athletes appraised the upcoming competition as a challenge.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".