Worthy and unworthy lives in education in emergencies: a comparative analysis of UN discourse on Ukraine and Venezuela
Bibliographic record
Abstract
Drawing on Judith Butler’s concept of grievability, this study highlights racialized hierarchies of attention within humanitarian aid, with a focus on the education in emergencies (EiE) sector, where responses to crises are divided along racial lines, deeming some populations as worthy and others as unworthy victims. We conduct a critical policy analysis of two contrasting cases – Ukraine, a crisis that has garnered extensive humanitarian attention and aid and media coverage, and Venezuela, among the world’s most neglected crises. From analysing 65 UNHCR and UNICEF documents, along with 14 key informant interviews, our findings reveal two dominant ways UN organizations classify populations as either worthy or unworthy of protection and support: through the framing of crises and the framing of affected populations. This study is among the first to empirically examine racialized hierarchies of attention and how they directly shape global attention, urgency, response, and resource distribution in EiE and the larger humanitarian sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".