From Theory to Research: Interpretational Guidelines, Statistical Guidance, and a Shiny App for the Model of Excellencism and Perfectionism
Bibliographic record
Abstract
After decades of research and debates about whether perfectionism is healthy or unhealthy, the Model of Excellencism and Perfectionism (MEP) recently differentiated between people striving for high standards (excellence strivers) and those pursuing perfectionistic standards (perfection strivers). In this study, we devised and tested an interpretational framework of nine scenarios to help determine whether perfectionism is beneficial, unneeded, or harmful by comparing the outcomes of excellence and perfection strivers. In a cross-sectional study with university students (N=271; Study 1), we found that perfection strivers savor positive school events less and have greater dropout intentions than excellence strivers. In a prospective/longitudinal design with college-aged athletes (N=296; Study 2), perfectionism was associated with higher athletic achievement. However, perfection strivers who failed to attain their goals experienced lower savoring and enjoyment than excellence strivers. Our findings highlighted the value of our interpretational scenarios as a hub to facilitate the comparison of MEP findings, while showing how to test MEP hypotheses with five popular statistical analyses. Furthermore, the MEP Shiny App is a valuable contribution to expedite the process of comparing the outcomes of excellence and perfection strivers. Overall, this research forged a substantive-methodological pathway that strengthens and enhances the practicality of the MEP. The MEP Shiny App is available at https://model-of-excellencism-and-perfectionism.shinyapps.io/Shiny_Version2/
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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 | 0.662 | 0.855 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.013 | 0.020 |
| Research integrity | 0.017 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".