Female exercisers' use of modeling: Who is observing and why?
Bibliographic record
Abstract
Modeling is recognized as an effective teaching and performance enhancement strategy in sport and motor skill learning settings. Recently Ste-Marie et al. (2012) proposed the Applied Model for the Use of Observation (AMUO) to guide research and application within sport and rehabilitation. Exercise is another movement environment where individuals may engage in observation for a variety of reasons; however, it is often overlooked within modeling research. The purpose of this study was to explore female exercisers' use of modeling within exercise environments. Ten female exercisers (Mage = 27.5 years, SD = 4.03) who regularly engage in both cardio and resistance training participated in semi-structured interviews. Verbatim interview transcripts were analyzed using a combination of inductive and deductive coding, with the AMUO as a framework for generating the higher order categories. Participants identified skill, emotional, and environmental factors that determined where they use modeling. They also described novel functions of modeling. Specifically, modeling was used not only for skill-based functions, but also for motivational, teaching, and social comparison/competition functions. These findings provide insight into how exercisers may opt to use observation as a strategy, what factors limit their use of modeling, and highlight areas for further research and development of the AMUO framework.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".