The Impact of Pre-Competition Estimated Results for Elite Archers on Performance According to Achievement Goal Theory
Bibliographic record
Abstract
This study aimed to evaluate the estimated/judged results for elite archers before competitions in the context ofAchievement Goal Theory and determine its impact on actual performance. Also, the study assessed the impact ofgoal orientation on the competition scores to comprehend the relationship between goal orientation and performance.Study participants were 116 elite archers who participated in Adult-Youth Indoor Turkey Championship in Izmir.Before the competition, the participants filled in the “Task and Ego Orientation in Sport Questionnaire”. Before thisscale, a survey form, developed by the researcher, was given to participants to learn about their personalcharacteristics. The participants were asked to make a note of the numbers they wore on the chest of their uniformsand the predicted/judged scores on the survey form. They were informed that at the termination of the competition,their actual scores and their predicted scores would be compared. The study presents two important results. The firstresult is related to the fact that athletes with high goal orientation were significantly more successful than those withlow goal orientation in a real competition environment. The other result of in the current study was the significantrelationship between the pre-competition predictions/estimates of individuals with high goal orientation and theircompetition performances. This study is significant because it demonstrated that individuals with higher goalorientations have higher performances and that their predictions/estimates for their performance are much moreaccurate.
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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 | 0.010 |
| 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) | 0.002 | 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".