Bibliographic record
Abstract
Drug development is widely recognized as risky, time consuming, and costly for pharmaceutical firms. Less widely appreciated is the fact that nonhuman animals and patients also bear risks and costs for pharmaceutical development. I argue that by participating in studies, nonhuman animals and patients commit some (or for nonhuman animals, all) of their welfare and labor to drive this process forward. I further argue that this commitment of welfare and labor is rendered invisible by discourses that present trial participation to patients as medical opportunity, despite evidence and principled reasons suggesting the contrary. This subsidy of welfare and labor, though nominal to moderate on a per patient basis, is significant when aggregated across patients and clinical trials and considered alongside nonhuman animal use. I close by arguing that this subsidy is grounded on defective consent, and that it generates two strong claims on researchers and states overseeing the research. The first is an obligation to economize on nonhuman animal and patient welfare and labor by conducting research efficiently. The second is an obligation to align drug development and policy with the aspirations that motivate the use of nonhuman animals and the motivations of patients in this endeavor.
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 | 0.042 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.083 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".