Defining the Limits of Emergency Humanitarian Action: Where, and How, to Draw the Line?
Bibliographic record
Abstract
Decisions about targeting medical assistance in humanitarian contexts are fraught with dilemmas ranging from non-availability of basic services, to massive demographic and epidemiological shifts, and to the threat of insecurity and evacuations. Aid agencies are obliged, due to capacity constraints and competing priorities, to clearly define the objectives and the beneficiaries of their actions. That aid agencies have to set limits to their actions is not controversial, but the process of defining the limits raises ethical questions. In MSF, frameworks for resource allocation are subject to constant reflection and reiteration, and perspectives are sought at all levels, from implementers at the programme level to the operational directors at headquarters. The perspectives of the programmes staff hold considerable weight as they have the knowledge and experience with particular communities to assess the degree of vulnerability and need, and are also the people who ultimately have to give explanations to beneficiaries when changes or closures are going to be instituted. Humanitarian agencies have a responsibility to ensuring that their workers are prepared to reflect on these dilemmas, and challenge the status quo when it costs lives.
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 | 0.073 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.024 | 0.056 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".