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Record W4401819346 · doi:10.1080/17441692.2024.2394811

Global health photography behind the façade of empowerment and decolonisation

2024· article· en· W4401819346 on OpenAlexaff
Arsenii Alenichev, Koen Peeters Grietens, Jonathan D. Shaffer, Sonya de Laat, Nassisse Solomon, Michael Parker, Halina Suwalowska, Patricia Kingori

Bibliographic record

VenueGlobal Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsWestern UniversityMcMaster University
FundersTrust for Mutual UnderstandingWellcome Trust
KeywordsEmpowermentMainstreamSociologyGlocalizationPhoto elicitationDecolonizationPhotographyPublic relationsPolitical scienceGlobalizationVisual artsPolitics

Abstract

fetched live from OpenAlex

Global health photography has historically been commissioned and, therefore, dominated by the gaze of Western photographers on assignments in the Global South. This is changing as part of international calls to decolonise global health and stimulate 'empowerment', spawning a growing initiative to hire local photographers. This article, based on interviews with global health photographers, reflects on this paradigm shift. It highlights how behind the laudable aim of 'empowerment' of local global health photography there is a simultaneous exploitation of precarious photographer labour and the emergence of 'glocal' photography elites. The paper argues that empowerment of local photographers can become a euphemism for reducing image production costs and maintaining control over the image content, while extending the scope of mainstream global health visual culture without challenging it. Finally, the article amplifies the growing concern that uncritical engagement with institutionalised empowerment becomes a warrant for the reproduction of local inequalities behind the fashionable façade of cooperation and care.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.041
Scholarly communication0.0050.006
Open science0.0010.008
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.026
GPT teacher head0.367
Teacher spread0.341 · 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.

Study designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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