Toward Person-Focused Assessment and Understanding the Human Need to Be Perfect: Commentary and Introduction to the Third Special Issue on Perfectionism
Bibliographic record
Abstract
In the current introductory article, we discuss the importance of balancing the variable-centered research in the perfectionism field with a person-focused approach. We examine the utility of a person-centered approach in assessment, research, and theory and the need to revisit overlooked themes central to understanding people who are extreme perfectionists. Our analysis focuses on addressing the core unaddressed issue of why perfectionists as unique individuals absolutely need to be perfect. We describe measures to assess individual differences in this need to be perfect and themes reflecting the need to be perfect that require investigation. The papers in this third special issue on perfectionism are then introduced and considered as examples of the merits of a broad approach that goes beyond trait perfectionism to also include perfectionistic self-presentation and the cognitive experience of perfectionism. We conclude by examining how certain variable-centered studies described in the current special issue yield insights about perfectionists as people when individuals are considered from a person-focused perspective.
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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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.035 | 0.077 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".