Do Athletes Imagine Being the Best, or Crossing the Finish Line First? A Mixed Methods Analysis of Construal Levels in Elite Athletes’ Spontaneous Imagery
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this article is to illustrate data transformation in a mixed methods research phenomenological study, investigating how athletes use concrete and abstract spontaneous imagery in and around competition. To achieve this, we combined the application of co-occurring codes and numerical transformation in a novel way. A thematic analysis of qualitative interviews with 12 elite athletes identified concrete imagery to focus on strategy generation, error correction, technique, and preparation, and abstract imagery to focus on desirability, symbolic and verbal representations, and regulation of affect, arousal, and mastery. Statistical analysis identified that subjective effectiveness of imagery significantly differed for sport type (reactive/static) and competition times. Researchers wishing to apply statistical analyses to qualitative data are encouraged to employ our methodology.
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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 | 0.051 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it