The Brain and Causality: How the Brain Becomes an Individual-Level Cause of Illness
Bibliographic record
Abstract
Abstract How do individual-level explanations become applied to social issues? Neurobiology – the study of the connections between behavior and the cells and structures of the brain – receives substantial public funding and influences social institutions, policy debates, and core aspects of human experience. With respect to mental health, neurobiology has ramifications for the way disorders are defined, diagnosed, and treated, along with how public funding for mental illness is allocated. This article addresses how neurobiologists establish the brain as a cause of mental illness. I analyze 17 months of ethnographic observation at a well-regarded neurobiological research laboratory, as well as observations at professional meetings, to detail three strategies: Linking the Brain to Mental Illness, Explaining Mental Illness with the Brain, and Asserting the Causal Importance of the Brain. These strategies first connect the brain to mental illness, and subsequently establish the causal primacy of the brain relative to alternative explanations (e.g., poverty). I connect findings to medical sociological theories, biological reduction, and emerging national health policies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".