Genetics, Neuroscience and Psychiatric Classification
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
Some psychiatrists anticipate a revolution in psychiatric nosology, on the basis of emerging data from genetics and genomics. There are, however, good empirical and conceptual reasons to resist any such revolution. Basing an understanding of psychiatric entities on one of multiple biological (not to mention sociocultural and psychological) considerations is a specious method of approaching the project of psychiatric taxonomy. A classification system that lacks sufficient consensus on the phenomenology of those classified cannot be adequately buttressed by exclusively genetic accounts. This paper advocates a more diversely informed nosology that, in turn, fosters attention to broader diagnostic considerations. We explore more plausible ways in which genetics and genomics, in conjunction with neuroscience and other biological disciplines, can help to shape diagnostic classification in psychiatry. There are, of course, differing views on the degree of prominence that genetics should take in psychiatric diagnosis and classification. We outline these accounts in illustration of this continuum. Drawing on Wimsatt's work on robustness analysis, we dismiss optimistic scenarios about the potential nosological advantages of psychiatric genetics and genomics, and offer a novel defense of realism about psychiatric entities. We also briefly sketch an integrative methodology for psychiatric research and classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it