The Taxonic Latent Structure and Taxometrics in Forensic Mental Health
Bibliographic record
Abstract
Recently, researchers in the field of forensic mental health have attempted to address the technical, empirical question of whether important clinical problems, such as psychopathy or malingering, constitute taxa (i.e., discrete conditions). In this paper, we provide a detailed elucidation of the foundational logic of the quantitative methods employed to answer this question, focusing on the taxometric procedures developed by Paul Meehl and colleagues. We attempt to demonstrate that research on taxonicity is hampered by (a) researchers' unfamiliarity with or misunderstanding of the logic underlying latent variable technologies and (b) the fundamental incapacity of Meehlian procedures to provide a test of taxonicity. We conclude by discussing the utility of taxometric procedures to research in forensic mental health and, more broadly, in the field of applied psychological measurement.
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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 | 0.042 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".