Recommendations for Adjudicating Among Alternative Structural Models of Psychopathology
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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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 | 0.359 | 0.760 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.016 | 0.008 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".