On the Distinction Between Personal Standards Perfectionism and Excellencism: A Theory Elaboration and Research Agenda
Bibliographic record
Abstract
Research on perfectionism is flourishing, but the unspecified distinction between perfectionism and the pursuit of excellence is a lingering issue that urgently needs conceptual, theoretical, and empirical attention. In this article, excellence and perfection are defined as distinct goals that form the basis of two different but related constructs. To move this idea forward, the term excellencism is introduced. Perfectionism and excellencism are defined and their similarities and differences are illustrated using symbolic logic and adjectives from the English lexicon. A point is made to clearly indicate that excellencism is a required reference point for reassessing the healthiness or unhealthiness of personal standards perfectionism. Using the law of diminishing returns as an analogy, a theory-driven rationale is proposed, and three alternative hypotheses are formulated. Showing that personal standards perfectionism is associated with better, comparable, and worse outcomes compared with excellencism offers the needed and sufficient conditions for respectively supporting the hypothesis that perfectionism is a healthy, unneeded, or deleterious pursuit. The propositions advanced in this theoretical article are more than incremental, and their practical implications are far-reaching: If personal standards perfectionism yields no added value or deleterious outcomes over and above excellencism, then excellence rather than perfection should be promoted.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".