Personality and participation in aerobics, circuit training, and Tai Chi
Bibliographic record
Abstract
The purpose of the present study was to compare the personality characteristics of 93 participants engaged in aerobics, circuit training, or Tai Chi for a period of six months or more using the Motivational Style Profile (MSP: Apter, Mallows, & Williams, 1998).Stepwise discriminant function analysis with Wilks' Lambda statistical criterion was employed in separate statistical analyses with the ten MSP subscales as predictors of membership of three exercise groups.Motivational Style Profile dominance scores produced non-significant discriminant functions.However, the MSP subscales produced significant discrimination between the three exercise groups.The most accurate prediction of correct group membership was 66.7% for aerobics, with 64.7% accuracy for circuit training, and 51.7% of correct classifications for Tai Chi.Discriminant function 1 separated the Tai Chi group from the other two exercise groups and discriminant function 2 differentiated the circuit training group from both Tai Chi and aerobics groups.The Tai Chi group had high levels of alloic-mastery and low levels of both autic-mastery and arousal-seeking.The circuit training group had high levels of negativism and arousal-seeking.The aerobics group had low alloic-mastery, and high autic-mastery in comparison to the Tai Chi group and lower levels of negativism and arousal-seeking than the circuit training group.The findings are important because they identified differences between the three activity groups rather than between exercise activity and non-activity groups.Also, MSP subscale scores were more sensitive discriminators of exercise group differences than their derived dominance values.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".