Exploring social disconnection through the lens of the model of excellencism and perfectionism
Bibliographic record
Abstract
Introduction. The Social Disconnection Model posits that perfectionism is positively associated with loneliness. However, past studies often failed to report the expected association between perfectionistic standards and indicators of social disconnection, such as loneliness. The recently developed Model of Excellencism and Perfectionism proposes that perfectionistic standards should be differentiated from the pursuit of high but realistic standards (i.e., excellencism). We conducted two studies to test the hypothesis that perfectionistic standards will be positively associated with loneliness once clearly separating them from excellencism. Method. In two studies (N = 284; N = 396), participants completed the Scale of Perfectionism and Excellencism and various measures of social disconnection. Results. In Study 1, perfectionism was positively related to loneliness, sacrificing social relationships, perfectionistic self-presentation, and less optimal social adjustment goals. Study 2 replicated and extended these findings, showing perfectionism was positively associated with loneliness and frustration of the need for relatedness. In contrast, excellencism was associated with lower loneliness and higher satisfaction of the need for relatedness. Discussion. The results show the importance of distinguishing between perfectionism and excellencism in unpacking the theoretically expected association between perfectionistic standards and psychological and social adjustment in the Social Disconnection Model. Overall, our findings provide novel insights into how perfectionism and excellencism are differentially linked to psychological experiences of social disconnection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".