Reflections on Three Decades of Research on Multidimensional Perfectionism: An Introduction to the Special Issue on Further Advances in the Assessment of Perfectionism
Bibliographic record
Abstract
In this article, we introduce this second special issue on the assessment of perfectionism along with an overview of developments in the perfectionism field over the past 30 years following the shift to studying perfectionism as a multidimensional construct. We examine some key contributions over the past decade, including the proliferation of meta-analyses and apparent rise over time in the prevalence of self-oriented, other-oriented, and socially prescribed perfectionism. We also outline what we consider to be seven definitive truths about the perfectionism construct and how these themes are reflected in the articles that follow. This special issue includes papers that describe abbreviated measures of existing perfectionism scales as well as new measures. Other papers in this special issue demonstrate the need to supplement a trait approach with a focus on cognitive perfectionism and to evaluate key mediators of the association between perfectionism and depression. Other research illustrates the usefulness of supplementing the predominant variable-focused approach with a person-centered approach. Collectively, the papers address several significant issues and outline key directions for future research in the next decade of research on multidimensional perfectionism.
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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 | 0.036 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".