Predicting weight-lifting imagery from drive for muscularity and impression motivation in male and female weight-trainers
Bibliographic record
Abstract
Imagery can be defined as using all the senses (e.g., sight, sound, feel, taste, smell) to create, or recreate, an experience in the mind, without actually experiencing the real thing (Vealey & Greenleaf, 2001). Although there have been many studies examining the use of imagery within an exercise setting (e.g., Gammage et al., 2000; Hausenblas et al., 1999), few studies have looked at the use of imagery within a weight-lifting population and its relation to body-related concerns. The purpose of this study was to examine the link between imagery use and two body-related concerns (drive for muscularity and impression motivation) amongst a weight-lifting population. Participants (N = 170), completed measures of drive for muscularity, impression motivation and imagery use. In order to determine if the three functions of imagery could be predicted from the drive for muscularity and impression motivation, controlling for age and gender, a series of 3 hierarchical multiple regression analyses were conducted. Results showed that all three forms of imagery (appearance, R2adj = .45, technique, R2adj = .27, and energy, R2adj = .29) could be predicted from drive for muscularity and impression motivation, after controlling for age and gender. Age, gender, impression motivation, and drive for muscularity were all significant predictors for all three functions of imagery. For appearance imagery, impression motivation accounted for 4% of the variance while drive for muscularity accounted for 2.6%. For technique imagery, impression motivation accounted for 3.6% of the variance and drive for muscularity 2.6%. Lastly for energy imagery, impression motivation accounted for 3.2% of the variance, while drive for muscularity was accounted for 4% of the total variance. These findings indicate that for weightlifters, increasing levels of drive for muscularity and impression motivation are associated with increased use of all three imagery functions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".