Bibliographic record
Abstract
Abstract The present study sought to describe the various reasons why athletes choose to manipulate the speeds of their images (i.e. image in slow motion, real-time, or fast motion). Athletes ( N = 9) were interviewed using a one-on-one, semi-structured interview format. All interviews were transcribed verbatim and content analyzed for themes. Results suggested that the particular image speed selected by an athlete does often serve a specific purpose. Slow-motion images were primarily employed to enhance the learning, development, review, or refinement of skills and strategies. Real-time imagery was employed when athletes wanted to accurately represent movement tempo, relative timing, or absolute movement duration in their images. Fast motion images were used to enable strategy planning during competition, to increase or maintain confidence perceptions, to energize athletes, and to increase imagery session efficiency and focus. Furthermore, regarding the use of multiple image speeds, athletes emphasized the importance of avoiding exclusively imaging in fast- or in slow-motion, making note of the importance of real-time image speed use in ensuring accurate mental representations of temporal aspects of performance. The findings of the current study indicate that the timing guideline of the PETTLEP approach to motor imagery (Holmes & Collins, 2001) may require revision. The use of any given image speed may be a matter of personal preference rather than one of functional necessity. One exception, however, does appear to be images focused on learning some temporal aspect of performance. In these instances, real-time speed seems to be a necessary characteristic of one’s image.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".