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Record W4297012368 · doi:10.17157/mat.9.3.5628

Anthropological Engagements with Global Health

2022· article· en· W4297012368 on OpenAlexaff
Priscilla Medeiros, Allyson Oliphant, Steven Farrow, Priyanka Gill

Bibliographic record

VenueMedicine Anthropology Theory · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsQueen's UniversityWestern UniversityWomen's College Hospital
Fundersnot available
KeywordsSyndemicColonialismGlobal healthSociocultural evolutionHealth careEconomic growthPolitical scienceMedicineSociologyHuman immunodeficiency virus (HIV)Family medicineAnthropology

Abstract

fetched live from OpenAlex

Epidemic infectious diseases like HIV/AIDS, tuberculosis, Ebola, and more recently COVID-19, have persistent and devastating impacts in human populations across the globe. In this Review essay, we consider together the monographs Epidemic Illusions (Richardson 2021) and Fevers, Feuds, and Diamonds (Farmer 2020), as well as the documentary film Bending the Arc (Davidson and Kos 2017), Together, they demonstrate the history of transnational colonialism, the significance of structural violence as a contributor to global health inequity, and the increasing presence of co-occurring epidemics worldwide, topics which are often absent from discussions of global health systems. These three works discuss epidemics as pathologies of history and sociocultural patterns of colonial dispossession in global health systems; the inclusion of patient narratives in two of them, the film Bending the Arc and the book Fevers, Feuds, and Diamonds, is pivotal in describing the intricacies of HIV infection and other infectious diseases, as well as the complexity of gaining control of syndemic diseases. Further, these three materials point to the importance of health education in communities and of access to healthcare by community members, and to the roles that health education and access play in health policy implementation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0710.000

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 head0.029
GPT teacher head0.395
Teacher spread0.366 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes1
Has abstractyes

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