Motivation for Sport: A Comparison Between Perfectionism and Excellencism in Self-Determination Theory
Bibliographic record
Abstract
Abstract Perfectionism creates motivational conflicts. However, researchers have repeatedly found positive associations between perfectionistic standards and autonomous motivation. One reason for this finding may be that researchers have not distinguished between perfectionistic standards and the pursuit of excellence, which may explain their somewhat unexpected results. 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 hypothesized 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 modeling 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 modeling. 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 rather than perfectionistic standards should be encouraged, as it is associated with autonomous motivation in sport. Examining motivation through the theoretical lens of the MEP revealed that pursuing high standards – rather than pursuing perfectionistic standards – is responsible for the desirable associations with autonomous motivation. Our findings with sport participants are relevant to recreational sporting activities in which people participate mostly out of autonomous motivation.Keywords: Perfectionism, Excellencism, Motivation, Sport
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".