The basic psychological needs in excellencism and perfectionism: A dual perspective with the need-as-motives and the need-as-nutriments frameworks
Bibliographic record
Abstract
Perfectionism has been theorized as a risk factor for psychological need frustration. However, past studies on basic psychological needs often reported ambiguous and unexpected findings for perfectionistic standards. The Model of Excellencism and Perfectionism (MEP) recently distinguished between perfectionistic standards and the pursuit of high yet attainable standards (excellencism). This study investigated their distinct associations with basic psychological needs, using measures taken from the need-as-motives and the need-as-nutriments perspectives. Young adults ( n = 305) completed the Scale of Perfectionism and Excellencism and various measures of need-related constructs. A multivariate multiple regression supported the hypothesis that excellencism and perfectionism are differentially linked with psychological needs. Excellencism was positively associated with three approach-oriented motives (need for achievement, affiliation, power) and satisfaction with the need for autonomy, relatedness, and competence. Conversely, pursuing perfectionistic standards was positively linked to two avoidance-oriented motives (e.g., fear of failure and losing control) and frustration with the three basic psychological needs. These findings reconcile research and theories by showing that pursuing perfection is not associated with adaptive psychological needs. Perfectionistic standards are linked to two avoidance-oriented motives (i.e., fear of failure and losing control) and frustration of basic psychological needs when properly distinguished from excellencism.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".