Bibliographic record
Abstract
Methadone Maintenance Treatment (MMT) in the United States has recently adopted an approach based on the principles of the Recovery movement — a view of treatment informed by addiction-as-disease models but also incorporating social, psychological, and spiritual components. Although organizations that administer drug treatment services claim that the shift represents a more client-centered, individualistic approach, it may not meet the needs of the many individuals who use MMT to reduce the harms of drug use, like overdose, rather than as a way to become abstinent. In this article, I use interview data from treatment providers to argue against institutional claims of Recovery as an individualistic model. My research demonstrates how — despite the wide variety of treatment goals among people on MMT — the Recovery discourse positions and organizes treatment strictly as abstinence-based, self-help. Moreover, I show how the Recovery model serves as the justification for an expansion of clinics’ ability to surveil and intervene in aspects of people’s lives which had previously been seen as outside of MMT’s purview, including nutrition, public service, and spirituality. In conclusion, I argue that Recovery restricts MMT’s ability to reduce harms, like overdose, in the lives of people who use drugs, and recommend that MMT adopt a more open-ended, low-threshold approach to treatment.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".