Bibliographic record
Abstract
Addressing global inequities has come to define a domain of activist medical anthropology called social justice studies. In a recent flagship volume, Dying for Growth: Global Inequality and the Health of the Poor, contributors describe the effect of global economic trends and neoliberal policies on the destitute and disadvantaged. In their advocacy, they use the voices and stories of the poor to explain the impact of structural adjustments. This paper uses Dying for Growth as an example through which to comment on the wider scholarly trend of using the local to validate global claims. The term "suffering stranger" describes those iconic figures whose experiences are presented in truncated first-hand accounts of suffering in order to validate broader theoretical aims. I argue that the suffering stranger masks the real absence of the voices of the poor and their suffering on the world stage. There is no international public sphere within which these voices might be heard; rather, there is a set of claims about justice and human rights. These claims, however, are themselves rooted in cultural values and are inextricably woven into global capital. I argue that, in using the voices of suffering to further a theoretical agenda, social justice activists assume the existence of a public, international domain within which those voices might be heard and that, in so doing, they further integrate the poor into destructive economic systems. Alongside the work of documenting health inequities, a truly effective activism may require assessing and critiquing existing claims of international morality.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.120 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".