An item response theory integration of normal and abnormal personality scales.
Bibliographic record
Abstract
The Diagnostic and Statistical Manual of Mental Disorders (DSM–IV–TR) currently conceptualizes personality disorders (PDs) as categorical syndromes that are distinct from normal personality. However, an alternative dimensional viewpoint is that PDs are maladaptive expressions of general personality traits. The dimensional perspective postulates that personality pathology exists at a more extreme level of the latent trait than does general personality. This hypothesis was examined using item response theory analyses comparing scales from two personality pathology instruments—the Dimensional Assessment of Personality Pathology-Basic Questionnaire (DAPP-BQ; Livesley & Jackson, in press) and the Schedule for Nonadaptive and Adaptive Personality (SNAP; Clark, 1993; Clark, Simms, Wu, & Casillas, in press)—with scales from an instrument designed to assess normal range personality, the NEO Personality Inventory–Revised (NEO PI-R; Costa & McCrae, 1992). The results indicate that respective scales from these instruments assess shared latent constructs, with the NEO PI-R providing more information at the lower (normal) range and the DAPP-BQ and SNAP providing more information at the higher (abnormal) range. Nevertheless, the results also demonstrated substantial overlap in coverage. Implications of the findings are discussed with respect to the study and development of items that would provide specific discriminations along underlying trait continua.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".