On the role of pre-competition emotions in elite karateka
Bibliographic record
Abstract
The purpose of the current study aimed to examine the performance related experiences of elite karate athletes prior to a major international competition. The secondary aim was to explore the relationship between athlete and coach performance ratings. Athletes selected emotional and non-emotional words to describe their experiences related to performance. Results indicated that best performance was characterized by high intensities of helpful emotion and non-emotion descriptors, while worst performance was characterized by high intensities of harmful descriptors. Moreover, intensities of emotions and non-emotions during actual good performance neared intensities recalled during best performance. Lastly, the coach reported athletes’ performance positively higher than the subjective rating from the athlete. Participants included five members of the Canadian National Wado-Kai karate team (n = 5) competing in international championships. Performance related experiences were assessed using the PBS-20 and ESP-40 questionnaire while recalling best and worst performance, and prior to kata and kumite performance. The participants and the coach rated their performance following competition. The Individual Zones of Optimal Functioning (IZOF) model posits that each individual athlete exhibits a unique constellation of emotion, non-emotions and intensities when experiencing peak, or dysfunctional, performance (Hanin, 2000). Practitioners are advised to provide interventions and tools that propel athletes to a pre-established zone of optimal functioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".