Bibliographic record
Abstract
In their Editorial Note accompanying JEiE Volume 10, Number 1, Samantha Colón, Nathan Thompson, and Dana Burde highlight the important contributions the authors featured in this issue make to education in emergencies scholarship and practice. In the research articles section, the contributing authors apply diverse, rigorous methodologies to practical questions in the education in emergencies field that relate to the opportunities and challenges of refining and scaling play-based learning; to the issues surrounding access to capacity-building initiatives for refugee teachers and for parents and caregivers in remote settings; and to the dynamics of intergroup contact, inclusion, and social hierarchy that are reflected in diverse learning spaces. The field notes section offers critical reflections on two adaptable, modular education in emergencies interventions: one is a place-based learning program centered on cultural heritage and young peoples' sense of belonging, and the other is a teacher wellbeing program based on building social-emotional competencies. Finally, the three book reviews featured in this issue highlight themes of belonging and connection to place, especially in the refugee experience, as well as stories of students and their communities being enabled to claim their agency, power, and a stake in a better future.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.032 | 0.028 |
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".