Self-Modeling and Competitive Beam Performance Enhancement Examined Within a Self-Regulation Perspective
Bibliographic record
Abstract
The observation of oneself on video that has been edited to show a performance level higher than one can actually perform is a feedforward form of modeling, termed self-modeling (SM; Dowrick, 1999 Dowrick, P. W. 1999. A review of self-modeling and related interventions. Applied and Preventive Psychology, 8: 23–29. [Crossref], [Web of Science ®] , [Google Scholar]). In this research, gymnasts alternated between viewing and not viewing a SM video during their competitive season. Results showed that gymnasts attained significantly higher beam scores when they viewed the video versus when they did not. No differences in self-efficacy were observed using a quantitative measure; however, a qualitative analysis of interviews based on Zimmerman's (2000) Ram, N. and McCullagh, P. 2003. Self-modeling: Influences on psychological responses and physical performance. The Sport Psychologist, 17(2): 220–241. [Crossref] , [Google Scholar] model, indicated that a number of self-regulatory processes, including self-efficacy, were employed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".