A Calculated Appeal: Infographics in the Image World of Maternal Mortality
Bibliographic record
Abstract
This article examines the prominence of infographics within the contemporary visual culture of global maternal health advocacy, exploring their aesthetic, narrative and semiotic power. Infographics are a ubiquitous sensory and aesthetic feature of the global health space, filling the pages of annual reports and websites of United Nations (UN), Non-Governmental organization (NGO) and government agencies and on display in the exhibition halls and power point presentations at international conferences. I focus on the social and political work that infographics do, observing the ways in which they go beyond their remit of conveying information and rendering complex numerical data in a neutral and accessible way. I begin by describing two key historical precedents in data visualization, highlighting the pioneering work of Florence Nightingale and W.E.B. Du Bois who used data visualizations as tools in their advocacy projects of social and institutional change. Infographics in the global maternal health advocacy space, I argue, are likewise calculated appeals, combining numbers with color and compelling imagery to move the viewer to awareness and action. Further, they tend to follow a contemporary neoliberal script that frames maternal survival in terms of investment, empowerment, and economic potential. In this way they shape how we understand the problem of maternal mortality and they legitimize solutions that can be taken up by policy makers and funders. This analysis contributes to broader anthropological conversations about visuality, biopolitics, and the humanitarian logic and procedural aesthetics of the contemporary global health enterprise.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".