EXPLOITATIONS AND THEIR COMPLICATIONS: THE NECESSITY OF IDENTIFYING THE MULTIPLE FORMS OF EXPLOITATION IN PHARMACEUTICAL TRIALS
Bibliographic record
Abstract
Human subject trials of pharmaceuticals in low and middle income countries (LMICs) have been associated with the moral wrong of exploitation on two grounds. First, these trials may include a placebo control arm even when proven treatments for a condition are in use in other (usually wealthier) parts of the world. Second, the trial researchers or sponsors may fail to make a successful treatment developed through the trial available to either the trial participants or the host community following the trial. Many commentators have argued that a single form of exploitation takes place during human subject research in LMICs. These commentators do not, however, agree as to what kind of moral wrong exploitation is or when exploitation is morally impermissible. In this paper, I have two primary goals. First, I will argue for a taxonomy of exploitation that identifies three distinct forms of exploitation. While each of these forms of exploitation has its critics, I will argue that they can each be developed into plausible accounts of exploitation tied to different vulnerabilities and different forms of wrongdoing. Second, I will argue that each of these forms of exploitation can coexist in single situations, including human subject trials of pharmaceuticals. This lesson is important, since different forms of exploitation in a single relationship can influence, among other things, whether the relationship is morally permissible.
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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 | 0.346 | 0.443 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.145 |
| Scholarly communication | 0.037 | 0.089 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.032 | 0.048 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".