Research Note: Ethics of Drug Treatment Research with Court-Supervised Subjects
Bibliographic record
Abstract
The last two decades have seen an acceleration of clinical research on, and treatment advances in, addictive illness. Much important research in this area requires the participation of subjects who themselves suffer from drug dependence and have a strong likelihood of becoming involved in the criminal justice system at some point. However, using court-supervised persons with addictive disorders in drug research raises a number of significant ethical issues. These include, among others, worries about the individual's ability to provide capable, voluntary, informed consent and the obligation of researchers to safeguard sensitive clinical information. A variety of potentially coercive factors can influence court-supervised persons in their decision whether to enter research and can compromise their ability to provide informed consent. In this paper, we explore the ethical issues arising in this research and offer some suggestions for approaches to address these concerns.
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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 | 0.049 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".