Bibliographic record
Abstract
The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition alternative model of personality disorder (AMPD) introduced innovations that are likely to be enduring features of a scientific classification of personality disorder: (a) an explicit definition of personality disorder, (b) a description of levels of severity, (c) a dimensional model of individual differences, and (d) the important principle that classifications should use and be consistent with the findings of normal personality science. Bach and Tracy's review of evidence of the clinical utility of the alternative model compared with traditional typal diagnoses clearly demonstrates that dimensional classification does not adversely affect clinical utility. Based on this evidence, they propose that the AMPD minus the hybrid component be adopted as the Diagnostic and Statistical Manual of Mental Disorders classification of personality disorder. It is argued that the recommendation is premature because evidence of clinical utility is insufficient in the absence of evidence of construct validity. The rest of the commentary explores the conceptual and methodological problems arising from the way the AMPD's innovative features are translated into a diagnostic classification. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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.
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.070 | 0.072 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".