Centering Local Knowledge to Address the Imbrication of Settler Colonialism and Global Health Comment on "The Rhetoric of Decolonizing Global Health Fails to Address the Reality of Settler Colonialism: Gaza as a Case in Point"
Bibliographic record
Abstract
In the article "The Rhetoric of Decolonizing Global Health Fails to Address the Reality of Settler Colonialism: Gaza as a Case in Point," Engebretsen and Baker call on researchers to re-examine the ways we employ the rhetoric of decolonization in global health. They critique the "reformist" strand of decolonization which fails to mitigate structural inequities resulting from settler colonialism. I extend the authors’ work by considering how researchers might harness decolonial approaches to identify and nuance the ways power relations, on a regional, national, or global level, lead to unnecessary suffering. I assert that this requires centering Indigenous voices and local knowledge and de-centering Eurocentric frameworks and presumed universality. My hope is that by being precise with the language we use to denounce atrocities, this will engender commitments and accountabilities that determine whether the response coming from global health leaders moves us towards increased health equity rather than empty rhetoric.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.054 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".