Motivation for sport: a comparison between perfectionism and excellencism in self-determination theory
Bibliographic record
Abstract
Perfectionism creates motivational conflicts. However, researchers have repeatedly found positive associations between perfectionistic standards and autonomous motivation. This may be because researchers have not distinguished between perfectionistic standards and the pursuit of excellence. Based on the Model of Excellencism and Perfectionism (MEP), we recruited 328 sport participants to investigate the unique associations of perfectionistic standards and excellencism with sport motivations. We hypothesised that perfectionistic standards should primarily be associated with controlled types of motivation and amotivation, while excellencism would only predict autonomous types of motivation. As expected, results of structural equation modelling showed that excellencism was a positive and significant predictor of intrinsic, integrated, and identified motivation. Mean scores of introjected motivation, external motivation, and amotivation were very low in this sample, thus we carried out zero-inflated negative binomial structural equation modelling. The probability of reporting zero introjected motivation, external motivation, and amotivation was positively associated with excellencism. The probability of reporting zero external motivation was negatively associated with perfectionistic standards. We concluded that pursuing excellencism should be encouraged, as it is associated with autonomous motivation in sport. Examining motivation through the MEP revealed that pursuing high standards – rather than perfectionistic standards – is responsible for the desirable associations with autonomous motivation. Our results suggest that findings from past studies should be interpreted with caution as they did not distinguish excellencism from perfectionistic standards.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".