Exploring Competitive Anxiety and Personality in Early Specializing and Sampling Pewee Boys Hockey Players
Bibliographic record
Abstract
Athletes who specialize early often invest more into their sport from a young age, thus it has been suggested early specializers may feel greater pressures to perform, and may have higher levels of anxiety. This study focused on better understanding competitive anxiety and personality, in relation to early specializers and non-early specializers. Hierarchal regression analyses revealed a significant relationship between CTA and CSA in Step 1. In Step 2, no significant additional variance was found for any of the predictor variables (i.e., OCEAN) or for the moderator variable (i.e., early specialization). In Step 3 no additional variance was accounted for by the interaction term for all predictor variables except agreeableness. The interaction of agreeableness and early specialization accounted for significant additional variance in CSA (R2 =. 035, p<. 05). Results highlight the need for future investigation into the role of personality and early specialization on CSA.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".