Deriving an Empirical Structure of Personality Pathology for DSM-5
Bibliographic record
Abstract
The DSM-IV model of personality disorders is composed of trait sets arranged into 10 theoretically distinct, polythetically assessed categories, with little regard for how the traits comprising these disorders are interrelated and structured. Research since the publication of DSM-III has shown that this model is untenable. The question is not whether this model needs revision; rather, the question is how to move from the existing DSM-IV framework to a model better connected with data. Empirically-based models of personality trait variation provide a starting point for DSM-5, and ongoing research will be used to delineate further the empirical structure of personality traits in the pathological range. The ultimate goal is to frame future DSMs in a way that is maximally useful for clinicians as well as researchers. It is also critical to understand that the DSM-5 is intended to be a living document that will facilitate novel inquiry and clinical applications, as opposed to a document designed to promote and perpetuate a fixed set of constructs. Thus, we view a proposed trait system as a first step on a path to a well-validated, clinically-useful structure.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".