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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".