Because Excellencism Is More than Good Enough: On the Need to Distinguish the Pursuit of Excellence from the Pursuit of Perfection
Bibliographic record
Abstract
An unresolved and controversial issue in the perfectionism literature is whether perfectionism is beneficial, harmful, or unneeded. The Model of Excellencism and Perfectionism (MEP) was recently developed to address this question by distinguishing the pursuit of perfection from the pursuit of excellence (Gaudreau, 2019). In this article, we report the results of the first empirical test of the core assumptions of the MEP. Across 5 studies (total N = 2,157), we tested the conceptual, functional, and developmental distinctiveness of excellencism and perfectionism. In Study 1, exploratory and confirmatory factor analyses with two samples supported the hypothesized two-factor structure of the newly developed Scale of Perfectionism and Excellencism (SCOPE). Study 2 provided evidence of convergent and discriminant validity from scores obtained from the SCOPE, and showed that, over and above excellencism, perfectionism was not associated with additional benefits (e.g., life satisfaction) or harms (e.g., depression). Studies 3-4 focused on the academic achievement of undergraduates and showed that, compared to excellence strivers, perfection strivers more often aimed for perfect A+ grades (Study 3), but in fact achieved worse grades (Study 4). Study 5 adopted a four-wave longitudinal design with undergraduates and showed that excellencism and perfectionism were associated with an upward and a downward spiral of academic development. Overall, the results support the core assumptions of the MEP and show that perfectionism is either unneeded or harmful.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| 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".