Migrations in Humanistic Therapy: Turning Drug Users into Patients and Patients into Healthy Citizens in Southwest China
Bibliographic record
Abstract
This article explores the translation and migration of illegal drugs, humanistic therapies and political ideologies by focusing on China’s first residential community drug treatment center, called Sunlight. I argue that the migration of contemporary treatment therapies from one continent to another initiates certain practices that re-appropriate and remake drug-using bodies that live and work at Sunlight. Reviewing Sunlight ethnographically also allows for broader theoretical exploration. When bodies do not operate under the common trope of possessive individualism different forms of biopolitical and therapeutic power are at play. In keeping with the theme of this special issue, this article begins with a discussion of why migration is a useful rubric for understanding how therapeutics and bodies become global entities and practices through the movement of three things: heroin, humanistic therapy and political ideology. It then presents an ethnographic slice of life at Sunlight to demonstrate how these practices and ideologies play out in the everyday. It finally returns to the question of why these therapies re-appropriate the post-socialist drug user’s body and psyche through a discussion of the term ‘psycho-sociability’. Psycho-sociability can be read as a demand for becoming a good biological citizen, as well as a theoretical rubric for explaining non-Western biopolitics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".