"Where they need me": the moral economy of international medical aid in Haiti
Bibliographic record
Abstract
This dissertation examines and analyzes the provision of international medical aid in northern Haiti. Medical aid in this setting comprises a wide range of goods and services, including pharmaceutical supplies and medical equipment, clinical procedures, support for infrastructure projects and training for Haitian medical staff. Based on ethnographic research conducted from 2007 to 2009 among the providers, implementors and recipients of medical aid, this study examines the various collaborations and tensions that result from the provision of health resources across marked social and economic inequalities. Beginning with a discussion of humanitarian heroism as exemplified by the lives and work of Drs. Albert Schweitzer and Paul Farmer, the dissertation goes on to describe the activities of a secular U.S.-based health NGO. The third chapter is devoted to the experiences and perspectives of Haitian medical residents at a large, public hospital, situating their lives and work within larger debates about obligation and emigration. The fourth chapter focuses on the issue of coordination, and examines the factors that impede coordination among the diverse actors involved in medical aid, despite unanimous calls for it. The final chapter proposes theories of moral economy, resentment and "ressentiment" as frameworks for understanding the diverse exchanges, values and emotions that constitute, influence and result from medical aid encounters. Rather than expose the shortcomings or failures of international medical aid in Haiti, this dissertation aims to highlight the ambivalence and contradictions experienced by the diverse actors involved in this complex process.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.040 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".