4.11-P16Understanding and operationalizing vulnerability in International Humanitarian Health Organisations
Bibliographic record
Abstract
Background: International humanitarian organisations (IHOs) providing health services have adapted policies and practices to address the needs of “vulnerable populations” in response to evidence that particular social groups are differentially affected by conflict, epidemics, emergencies and disasters. However, there has been little critical examination of how “vulnerability” is understood and operationalised, and what the ethical implications of this categorisation are. Methods: This study assembles IHO policy and other guidance on services for “vulnerable populations”; and assembles literature on how humanitarian health workers (HHWs) and aid recipients identified as ‘vulnerable’ understand their experiences in moral terms. In future we will administer surveys and conduct qualitative interviews with stakeholders in IHOs and aid recipients identified as “vulnerable” to investigate their moral experiences. We examine how “vulnerability” is conceptualised in IHO policy, guidance, and eventually, practice; how vulnerable groups and persons are identified and how services are refined for their needs; how different vulnerabilities are prioritised, and why; whether constructions of “vulnerability” threaten the moral agency of recipients, and perpetuate damaging stereotypes; and whether ascriptions of vulnerability affect trust between health workers and vulnerable groups. Results: Preliminary results suggest a pervasive failure to consult members of vulnerable groups and/or their representative organisations during crisis response. As a result, they experience epistemic injustice, which concerns power asymmetries in the “credibility economy”. Conclusions: Further research is needed on how vulnerability is understood and operationalised in humanitarian responses. Findings suggest that ideas and practices surrounding vulnerability lack coherence, and may lead to certain groups experiencing epistemic and other injustice. Main messages: Critical examination of how “vulnerability” is understood, operationalised in humanitarian crises, and what its ethical implications are is lacking. Preliminary evidence suggests that ideas and practices surrounding vulnerability lack coherence and that certain groups experience epistemic injustice and distrust.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".