Self-Regulatory Processes Employed During Self-Modeling: A Qualitative Analysis
Bibliographic record
Abstract
Self-modeling involves the observation of oneself on an edited videotape to show a desired performance (Dowrick & Dove, 1980). While research has investigated the effects of self-modeling on physical performance and psychological mechanisms in relation to skill acquisition (e.g., Clark & Ste-Marie, 2007), no research to date has used a qualitative approach to examine the thought processes athletes engage in during the viewing of a self-modeling video in a competitive sport environment. The purpose of this study was to explore the self-regulatory processes of ten divers who viewed a self-modeling video during competitions. After competition, the divers were asked four questions relating to the self-modeling video. Zimmerman’s (2000) self-regulation framework was adopted for deductive analysis of the responses to those questions. The results indicated that a number of self-regulatory processes were employed, and they were mainly those in the forethought (75%) and self-reflection (25%) phases of Zimmerman’s model. Directions for future research in self-regulation and self-modeling are discussed.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".