An integrative evolutionary framework for psychopathology
Bibliographic record
Abstract
Abstract The field of psychopathology is in a transformative phase, and is witnessing a renewed surge of interest in theoretical models of mental disorders. While many interesting proposals are competing for attention in the literature, they tend to focus narrowly on the proximate level of analysis and lack a broader understanding of biological function. In this paper, we present an integrative framework for mental disorders built on concepts from life history theory, and describe a taxonomy of mental disorders based on its principles, thefast–slow–defensemodel (FSD). The FSD integrates psychopathology with normative individual differences in personality and behavior, and allows researchers to draw principled distinctions between broad clusters of disorders, as well as identify functional subtypes within current diagnostic categories. Simulation work demonstrates that the model can explain the large-scale structure of comorbidity, including the apparent emergence of a general “p factor” of psychopathology. A life history approach also provides novel integrative insights into the role of environmental risk/protective factors and the developmental trajectories of various disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".