Effects of self‐modeling on figure skating jump performance and psychological variables
Bibliographic record
Abstract
Abstract This study investigated whether self‐modeling plus physical practice would improve intermediate level figure skaters’ jump performance, as well as their self‐efficacy, motivation, and state anxiety, when compared to physical practice alone. Twelve female figure skaters ( M =13.4 years of age, SD =1.4) participated in a within‐participant design where they received a self‐modeling intervention for one jump and a control condition for another jump. They were also compared with a separate control group of 7 skaters ( M =14.2 years of age, SD =2.35) who received no intervention. We hypothesized that skaters would show greater improvement in physical and psychological performance scores for jumps in the self‐modeling condition than for jumps in the control conditions. We also hypothesized that increased self‐efficacy and motivation and decreased state anxiety would mediate the relationship between self‐modeling and physical performance. Counter to our predictions, no differences existed between the two conditions for the self‐modeling group or between the self‐modeling group and the control group. Despite the lack of statistical support for our hypotheses, skaters’ evaluation of the intervention was very positive and suggests possible explanations for the results.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".