Bibliographic record
Abstract
The alcohol and other drug field is characterized by great diversity in kinds of treatment and treatment philosophies. Even the kinds of problems treatment is expected to address vary significantly, although agreement seems to exist that the general purpose is to help people “get better.” This article considers this diversity, drawing on a qualitative project conducted in three countries: Australia, Canada, and Sweden. Inspired by the project’s multisite approach and the questions it raises about comparative research, the article critically engages with the notion of “comparison” to think through what is at stake in making comparisons. Analyzing 80 interviews conducted with policy makers, service providers, and peer advocates, the article maps key ways treatment is conceptualized, identifying in them a central role for comparison. Participants in all sites invoked the need to consider addiction a multifaceted problem requiring a mix of responses tailored to individual differences. Related notions of “holism” were also commonly invoked, as was the need to concentrate on overall improvements in well-being rather than narrow changes in consumption patterns. In conducting this analysis, this article poses a series of critical questions. What kinds of comparisons about quality of life, the self, and well-being do treatments for addiction put into play? What categories and criteria of comparison are naturalized in these processes? What kinds of insights might these categories and criteria authorize, and what might they rule out? In short, what does it mean to understand alcohol and other drug use and our responses to it as intimately intertwined with the need to “get better,” and what happens when we scrutinize the politics of comparison at work in getting better through addiction treatment? We conclude by arguing for the need to find new, fairer, ways of constituting the problems we presently ascribe to drugs and addiction.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 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".