Bibliographic record
Abstract
Imagery use in sport has been increasingly researched over the past few decades. Despite the recognition that athletes can benefit from employing imagery, there has been limited research investigating factors (e.g., sport type, competitive level) that influence athletes' use of imagery. One factor that has recently received some limited attention has been coaches' influence on athletes' imagery use (Munroe, Hall, Simms, & Weinberg, 1998). Research has suggested that coaches support the use of many psychological techniques (e.g., goal setting, imagery, team building), and they believe imagery is the most useful. Therefore, they employ it most frequently with their athletes (Hall & Rodgers, 1989). A recent study by Jedlic (2003) examined athletes' perceptions of coaches' encouragement of imagery use. The results suggested that coaches do have a significant influence on athletes' imagery use. The present study examined coaches' encouragement of athletes' use of mental imagery. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .H35. Source: Masters Abstracts International, Volume: 44-01, page: 0338. Thesis (M.H.K.)--University of Windsor (Canada), 2005.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".