Editorial: From <i>Amrita</i> to Substance D: Psychopharmacology, Political Economy, and Technologies of the Self
Bibliographic record
Abstract
In this issue of Transcultural Psychiatry, we present papers from the 9th Annual Advanced Study Institute in Cultural Psychiatry, ‘Psychopharmacology in a Globalizing World,’ which took place in Montreal, June 12–15, 2007. Recent years have seen a dramatic increase in the use of psychiatric medications among children, adolescents, and adults in many countries. Psychopharmaceuticals are big business and are at the center of the globalization of psychiatry. Psychiatric knowledge and practice as well as the experience of patients taking medication are shaped by the interests of pharmaceutical companies and by wider social and cultural attitudes toward medication. This meeting brought together scholars from health and social sciences to examine the cultural shaping and consequences of the use of psychiatric medications. Presentations addressed marketing and the political economy of psychopharmacology, the production of scientific ‘evidence’ and professional practices, the medical and nonmedical uses of medications, popular attitudes toward medication, and phenomenological pharmacology. Medications can be said to have social lives in several senses. They are important commodities, widely consumed and generating huge profits for multinational corporations. This consumption is fostered by active marketing, spurred on by changes in popular culture that promote the use of medications to manage both psychiatric disorders and milder forms of distress. Psychiatric practitioners and institutions contribute to this
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".