Still Measuring Perfectionism After All These Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The perfectionism field has advanced considerably over the past 25 years, but researchers typically focus on substantive findings, and there has been comparatively little systematic emphasis on measurement issues. This special issue introduces new perfectionism measures and examines several important measurement topics. This special issue advances the theme that how constructs are conceptualized and measured has a direct impact on the findings that emerge in empirical research. We provide an overview of specific topics addressed in this special issue, including the importance of distinguishing between perfectionism versus conscientiousness and the role of assessment in documenting the heterogeneity that exists among people who all describe themselves as perfectionists. It is evident from the papers in this special issue that the complexities inherent in the perfectionism construct require an equally complex and sophisticated measurement approach. Further advances in the perfectionism field depend largely on implementing a programmatic approach to measurement and assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it