Is perfectionism a killer of creative thinking? A test of the model of excellencism and perfectionism
Bibliographic record
Abstract
The standards that a person pursue in life can be set in a rigid or flexible way. The recent literature has emphasized a distinction between high and realistic standards of excellence, from high and unrealistic standards of perfection. In two studies, we investigated the role of striving towards excellence (i.e., excellencism) and striving towards perfection (i.e., perfectionism) in relation to divergent thinking, associative thinking, and openness to experience, general self-efficacy, and creative self-beliefs. In Study 1, 279 university students completed three divergent thinking items, which called for creative uses of two common objects and to name original things which make noise. A measure of openness to experience was included. Results from multiple regression indicated that participants pursuing excellence tended to generate more answers and more original ones compared with those pursuing perfection. Openness to experience was positively associated to excellencism and negatively associated to perfectionism. In Study 2 (n = 401 university students), we replicated these findings and extended them to associative tasks requiring participants to generate chains of unrelated words. Additional individual differences measures included general self-efficacy, creative self-efficacy, and creative personal identity. The results suggested that excellencism was associated with better performance on divergent thinking and associative tasks, compared with perfectionism. Excellencism was positively associated with all four personality variables, whereas perfectionism was significantly and negatively associated with openness to experience only. Implications for the distinction between perfectionism and excellencism with respect to creative indicators are discussed. In addition, the paradoxical finding that perfection strivers had high creative self-efficacy and creative personal identity but lower openness to experience and poorer performance on objective indicators of creative abilities is discussed.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".