Beyond Numbers: The Use and Usefulness of Data for Education in Emergencies
Bibliographic record
Abstract
Recognizing the lack of knowledge about how to improve data systems for education in emergencies (EiE), we examine in this article how EiE professionals use data and what makes data "useful" to them. Drawing from 48 semistructured interviews from a purposive sample of professionals working in the EiE field across the humanitarian, development, and stabilization sectors, we explored the primary ways EiE professionals use data. Using inductive and emergent coding, we identified the key themes, which we then disaggregated by participants' sector and role in EiE operations. Our findings indicate that there is a common need across sectors for data that inform operations. However, participants working at a national or local level spoke the most about operational uses of data and the least about strategic uses, such as policymaking and advocating. Meanwhile, there was a notable emphasis among actors at the global level on strengthening data systems and their strategic uses. In this article, we also highlight the myriad nontechnical factors that shaped participants' perceptions of usefulness, including the politicization of data, users' expertise in analysis, and personal and institutional relationships. We argue that conversations about improving data for use in EiE must not focus exclusively on tools or techniques but also on people, institutions, and contexts.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".