Journal on Education in Emergencies: Volume 10, Number 1 (Complete)
Bibliographic record
Abstract
In 2024, 295 million school-age children worldwide lived in conflict-affected and fragile countries, and more than 103 million children were unable to attend school-an increase of 31 million since 2023 (Save the Children 2024).As of December 2024, children made up an estimated 40 percent of the global refugee population and 49 percent of internally displaced people (UNICEF 2024).With the intensification of conflict, poverty, and environmental disasters, it is expected that displacement will continue to increase.This will aggravate the education crisis and leave many more displaced children without access to school, while those who are in school will continue to struggle to meet basic learning standards.These statistics underscore the deepening crises affecting children worldwide while stressing the need for immediate, targeted interventions to safeguard their right to an education.Despite the overwhelming challenges-growing education gaps, increased violence and exploitation-many global efforts on the part of policymakers, program planners, scholars, and advocates remain focused on finding solutions and providing hope.The Journal on Education in Emergencies (JEiE), now in its tenth year of publication, continues to offer free, open-access, peer-reviewed discussions of the education challenges facing students and other stakeholders worldwide, and of programs and policies that may support their learning, development, wellbeing, and future livelihoods.The five research articles, two field notes, and three book reviews presented in JEiE Volume 10, Number 1 explore questions about displacement, identity, and the right to belong in Jordan, Nigeria, Palestine, and the United States.They review initiatives in Colombia and Ethiopia that focus on teachers' wellbeing, training, and professionalization, as well as the opportunities and challenges of refining and scaling-up play-based learning for refugee and host communities in Ethiopia, Lebanon, Tanzania, and Uganda.The authors featured in this issue also provide new insights into the displacement, migration, and resettlement experiences of populations from Iraq, South Sudan, Somalia, Syria, and beyond, and join debates centered on the agency, power, and deservingness of vulnerable, marginalized, and crisis-affected groups.These debates are particularly important as US President Donald Trump begins his second administration and political tensions increase around such issues as internationalism and isolationism, migration and belonging.Meanwhile, the recent wave of rejections of incumbent leaders and political parties worldwide has exacerbated the uncertainty of the global political landscape.Against this backdrop, the scholars who contributed to this issue examine urgent and ongoing questions about how to ensure safe, effective, and quality education for all students amid political and social forces that may be antithetical
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.033 |
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".