Perfectionism, excellencism, and impostor feelings: a meta-analysis and two studies on the moderating role of research productivity in graduate students
Bibliographic record
Abstract
Previous research examining the link between perfectionistic standards and impostor feelings has yielded mixed results. Responding to recent recommendations to distinguish between the pursuit of high standards (excellencism) and perfectionistic standards (perfectionism), this research reexamined these relationships across three studies. Study 1, a meta-analysis (k = 20), found that proxy measures of perfectionism (e.g. self-oriented perfectionism) but not proxy measures of excellencism (e.g. high standards) were significantly associated with impostor feelings. Study 2, conducted with graduate students (N = 372), revealed that perfection strivers experienced more impostor feelings than excellence strivers when dissatisfied with their research productivity. Study 3 (N = 388) replicated these findings with a measure of annual research productivity and relied on three hypothetical feedback scenarios to explore how perfection and excellence strivers react during the peer review process. Results showed that perfection strivers experienced higher impostor feelings than excellence strivers when research productivity is low and during hypothetical scenarios of receiving positive and mixed feedback. Our findings suggest that pursuing perfection beyond excellence is detrimental, as it heightens impostor feelings in graduate students when they fail to meet their idealized standards.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.023 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".