Recommendations for Adjudicating Among Alternative Structural Models of Psychopathology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Historically, researchers have proposed higher-order factors to explicate the structure of psychopathology, including Externalizing, Internalizing, Fear, Distress, Thought Disorder, and a general factor. Despite extensive research in this domain, the underlying structure of psychopathology remains unresolved. Here, we examine several issues in adjudicating among structural models of psychopathology. Using simulations and analyses of the extant literature, we contrast the model-based reliability of alternative structural models of psychopathology and highlight shortcomings of conventional model-fit indices for such adjudication. We propose alternative criteria for evaluating and contrasting competing structural models, including various model characteristics (e.g., the magnitude and consistency of factor loadings and their precision), the consistency and sensitivity of factors to their constituent indicators, and the variance explained in and patterns of associations with relevant variables. Using these criteria as adjuncts to conventional fit indices should become standard practice and will greatly facilitate adjudication among alternative structural models of psychopathology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it