In search of the self-determination continuum in exercise: A smallest space analysis
Bibliographic record
Abstract
The objective of this study is to examine how motivation is structured and computed according to Deci and Ryan's (1985, 1991) self-determination continuum hypothesis. To reach this end, data gathered with the help of the Behavioral Regulation in Exercise Questionnaire-2 (Markland & Tobin, 2004) was analyzed using smallest space analyses (SSA). Results indicate the presence of a simplex pattern suggesting that one dimension (i.e., self-determination continuum) seems to underlie the motivations postulated by SDT in the exercise domain. However, results from a two-dimensional SSA show that a duplex is also present within the BREQ-2. Finally, results indicate that the weighting system used by SDT researchers may be slightly underestimating relationships between exercise motivation and some of its correlates. Results are discussed with respect to various theoretical, conceptual, and methodological considerations when measuring motivation in sport/exercise.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".