The “Elephants in the Room” in U.S. global health: Indigenous nations and white settler colonialism
Bibliographic record
Abstract
We are two Indigenous young people from the Oceti Sakowin, made up of the Lakota and Dakota Tribal Nations in modern day U.S. and Canada.As Indigenous health professionals and students, we are used to having to advocate for visibility in healthcare.In medical schools, students learn little of us and there are few to no Indigenous faculty [1].In the academic literature, we are often left out of studies, creating a cycle of data inequity [2].And in global health, Indigenous Nations are seldom considered, even in decolonizing global health spaces.Earlier this year, we realized just how rooted our invisibility in global health is when we found ourselves unsuccessfully attempting to convince three white U.S. global health physicians to center and include Indigenous Peoples in a decolonizing global health paper.Instead, we were told to make a separate paper on "Indigenous issues."When we asked the physicians to join us on the separate paper, we were told the paper would be more powerful coming from Indigenous authors alone, even after explaining the potential power of white allies joining Indigenous academics on papers that challenge the status-quo.After being siloed from the mainstream conversation on decolonizing global health, we felt this experience was a microcosm of larger systemic erasure of Indigenous Nations and white settler colonialism in U.S. global health and the decolonizing global health space.When most U.S physicians consider the term "global health" or even the term "decolonizing global health," they think of low-and middle-income countries (LMIC) outside their borders, usually in the global south.While countries outside U.S. borders should absolutely be included, rarely are the over 567 Indigenous Nations within U.S. borders considered, each with their own cultures, languages, spiritualities, and political systems predating colonization by thousands of years.In the field of U.S. global health and the decolonizing global health movement, Indigenous Nations and settler colonialism in the U.S. remain the elephants in the room.From our perspective, Indigenous erasure in global health is due to the lack of recognition of our status as sovereign Nations within a high-income country that colonized us, creating massive health disparities and social determinant of health measures on par with low-and middle-income countries (LMIC) in the global south [3].Over the past 500 years, colonization weakened Indigenous systems that helped to maintain community health (e.g.traditional food systems, access to clean water, Indigenous languages, access to land) and replaced them with unsupported and underfunded systems, leading to disproportionate systemic health disparities, including some of the highest rates of diabetes, suicide, and cardiovascular diseases [4,5].
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".