A Theoretical Framework for a Comprehensive Approach to Medical Humanitarianism
Bibliographic record
Abstract
This article aims to demonstrate how the impact of humanitarian crises on health outcomes is related to social justice issues, even when these crises are brought upon by natural disasters. Pre-existing inequalities between individuals and social groups within a community affect in important and complex ways the health disparities which result from natural disasters. Drawing on the thought-provoking work of Paul Farmer, my main hypothesis is that socio-political factors prior to natural disasters determine ‘structured health risks’ that humanitarian crises will necessarily exacerbate. To adequately respond to these structured health risks, medical humanitarianism cannot abide by an apolitical approach which mainly focuses on emergency relief. A more comprehensive analysis of the socio-political aspects of the health impact of humanitarian crises indicates that a more comprehensive approach to medical humanitarianism is necessary. This has three implications. First, a coherent account of medical humanitarianism needs to assess the international dimension of structural injustice that leads to structured health disparities. Second, this comprehensive approach to medical humanitarianism supports the ‘denaturalization of natural disasters’ argument. Third, medical humanitarianism should be organized around a broader and more complex approach of overlapping sequences which bridge emergency relief, reconstruction and development through a better aligned, orchestrated and coherent international effort.
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 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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".