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Record W4283832806 · doi:10.1371/journal.pgph.0000719

The “Elephants in the Room” in U.S. global health: Indigenous nations and white settler colonialism

2022· article· en· W4283832806 on OpenAlexaboutno aff
Anpotowin Jensen, Victor A. Lopez-Carmen

Bibliographic record

VenuePLOS Global Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIndigenousWhite (mutation)EthnologyPolitical scienceGeographyHistoryGender studiesAnthropologySociologyArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

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].

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.019
Scholarly communication0.0040.004
Open science0.0000.005
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.150
GPT teacher head0.449
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations21
Published2022
Admission routes1
Has abstractyes

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