Effects of self‐modeling on figure skating jump performance and psychological variables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 it