People-Centered Accountability amid the Gaza Genocide: Doctors Against Genocide, Healthcare Workers Watch, and the Freedom Flotilla Coalition.
Bibliographic record
Abstract
This paper examines how people-centered accountability initiatives are operating to enforce the right to health amid Israel's genocide in Gaza. Drawing on a critical case study of Doctors Against Genocide, Healthcare Workers Watch, and the Freedom Flotilla Coalition, we situate these actors' work within international human rights law, social accountability scholarship, and decolonial and abolitionist critiques. We show how these actors are able to combine clinical documentation, survivor testimony, and direct action to monitor human rights violations, generate medically literate records of the harm inflicted, and press for remedies that state-centered mechanisms have failed to deliver despite findings of war crimes and genocide by United Nations bodies and human rights groups. Across these cases, we identify some common practices and tensions surrounding coalition-building, risks to documentation, navigating a media environment of mis/disinformation, and engaging strategically with institutions that often reproduce health harms or are directly complicit. We argue that these movements treat people-centered accountability as part of their professional duty and act on a mandate to prevent mass atrocity crimes rather than being silent. We conclude by outlining some practical implications for clinicians, professional associations, and health systems seeking to align their global health practice with a people-centered approach to accountability.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.049 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".