Toward an Empirically Based Classification of Personality Disorder
Bibliographic record
Abstract
A framework for an empirically based classification of personality disorder is proposed that has two components: (a) a definition of personality disorder, and (b) a scheme for describing individual differences in personality disorder traits. It is suggested that the diagnosis process should begin by establishing the presence of personality disorder and then proceed to a description of the personality on a set of trait dimensions. It is argued that a definition of personality disorder should reflect an understanding of the nature of the "harmful dysfunction" implied by a diagnosis of personality disorder. With this approach, personality disorder is defined as the failure to solve life tasks involving the development of integrated representations of self and others, and the capacity for adaptive kinship and societal relationships. The second component of a classification is a system to describe individual differences. It is suggested that these should be based on taxonomies of normal and disordered traits, and that the classification incorporates both higher-order patterns and more specific basic traits. Given that personality appears to be inherited as a large number of genetic dimensions, it is suggested that the primary level for describing individual differences is that of the basic or lower-level traits rather than broader or higher-level traits used in descriptions of normal personality.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".