JUSTIFIED COMMITMENTS? CONSIDERING RESOURCE ALLOCATION AND FAIRNESS IN MÉDECINS SANS FRONTIÈRES‐HOLLAND
Bibliographic record
Abstract
Non-governmental aid programs are an important source of health care for many people in the developing world. Despite the central role non-governmental organizations (NGOs) play in the delivery of these vital services, for the most part they either lack formal systems of accountability to their recipients altogether, or have only very weak requirements in this regard. This is because most NGOs are both self-mandating and self-regulating. What is needed in terms of accountability is some means by which all the relevant stakeholders can have their interests represented and considered. An ideally accountable decision-making process for NGOs should identify acceptable justifications and rule out unacceptable ones. Thus, the point of this paper is to evaluate three prominent types of justification given for decisions taken at the Dutch headquarters of Médecins sans Frontières. They are: population health justifications, mandate-based justifications and advocacy-based justifications. The central question at issue is whether these justifications are sufficiently robust to answer the concerns and objections that various stakeholders may have. I am particularly concerned with the legitimacy these justifications have in the eyes of project beneficiaries. I argue that special responsibilities to certain communities can arise out of long-term engagement with them, but that this type of priority needs to be constrained such that it does not exclude other potential beneficiaries to an undesirable extent. Finally, I suggest several new institutional mechanisms that would enhance the overall equity of decisions and so would ultimately contribute to the legitimacy of the organization as a whole.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".