Bibliographic record
Abstract
In this paper, I explain why evolutionary psychiatry is not where the next revolution in psychiatry will come from. I will proceed as follows. Firstly, I will review some of the problems commonly attributed to current nosologies, more specifically to the DSM. One of these problems is the lack of a clear and consensual definition of mental disorder; I will then examine specific attempts to spell out such a definition that use the evolutionary framework. One definition that deserves particular attention (for a number of reasons that I will mention later), is one put forward by Jerome Wakefield. Despite my sympathy for his position, I must indicate a few reasons why I think his attempt might not be able to resolve the problems related to current nosologies. I suggest that it might be wiser for an evolutionary psychiatrist to adopt the more integrative framework of “treatable conditions”. As it is thought that an evolutionary approach can contribute to transforming the way we look at mental disorders, I will provide a brief sketch of the basic tenets of evolutionary psychology. The picture of the architecture of the human mind that emerges from evolutionary psychology is thought by some to be the crucial backdrop to identifying specific mental disorders and distinguishing them from normal conditions. I will also provide two examples of how evolutionary thinking is supposed to change our thinking about some disorders. Using the case of depression, I will then show what kind of problems evolutionary explanations of particular psychopathologies encounter. In conclusion, I will evaluate where evolutionary thinking leaves us in regard to what I identify as the main problems of our current nosologies. I’ll then argue that the prospects of evolutionary psychiatry are not good.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".