That men should put an enemy in their mouths to steal away their brains: Reconsidering the origins of model psychosis
Bibliographic record
Abstract
The promises of the Prozac century have fallen short; the number of novel, therapeutically significant medications successfully completing development shrinks every year; and the demand for better treatments constantly grows. Answering these hardships is a renewed optimism concerning the efficacy of controlled psychedelic therapy, a renaissance that has seen the resurgence of a familiar concept: intoxication as model psychosis. And yet, little has been made of where this peculiar idea originates. Why did we come to liken psychosis to intoxication, and why is this an idea we find so hard to shake? Questioning the conventional narrative that identifies the concept as emerging in the mid 19th century, this article seeks to uncover the conceptual foundations underlying what is now intended by ‘model psychosis’. This investigation begins with an assessment of both Moreau de Tours’s concept of hashish madness in 1845 and Emil Kraepelin’s study of artificial insanity in the 1880s–90s. In seeking to contextualize these ideas, this article further considers the deeper historical association between intoxication and psychosis, instead proposing that intoxication represents an originary conception of madness. Bringing this examination into the 19th century, it becomes apparent that perceptions of intoxicants, and intoxication, were immanently participatory in the emerging understanding of psychosis. The contemporary understanding of model psychosis comes into focus when these elements coalesce with the advent of psychological modelling. Ultimately, the goal is not merely to understand how and why model psychosis became thinkable, but to examine how overlooked concepts have engendered new ways of being neuro-psychiatric subjects.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".