A failure in solidarity: Ethical challenges in the development and implementation of new tuberculosis technologies
Bibliographic record
Abstract
Prominent tuberculosis (TB) actors are invoking solidarity to motivate and justify collective action to address TB, including through intensified development and implementation (D&I) of technologies such as drugs and diagnostics. We characterize the ethical challenges associated with D&I of new TB technologies by drawing on stakeholder perspectives from 23 key informant interviews and we articulate the ethical implications of solidarity for TB technology D&I. The fundamental ethical issue facing TB technological D&I is a failure within and beyond the TB community to stand in solidarity with persons with TB in addressing the complex sociopolitical contexts of technological D&I. The failure in solidarity relates to two further ethical challenges raised by respondents: skewed power dynamics that hinder D&I and uncertainties around weighing risks and benefits associated with new technologies. Respondents identified advocacy and participatory research practices as necessary to address such challenges and to motivate sustained collective action to accelerate toward TB elimination. We present the first empirical examination of bioethical accounts of solidarity in public and global health. Our study suggests that solidarity allows us better to understand and address the ethical challenges that arrest the D&I of new TB technologies. Solidarity lends credence to policies and practices that address the relational nature of illness and health through collective action.
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 | 0.103 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.050 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".