Crisis Upon Crisis: Refugee Education Responses Amid COVID-19
Bibliographic record
Abstract
This study applies a critical political economy approach to understand the tensions, contradictions, and inequities that emerged when COVID-19 altered narratives and practices in education in emergencies, at the global policy level and within the local context of Syria refugee education in Lebanon. Through a vertical case study methodology, our research offers insights into a setting in which global organizations and actors sought to address the COVID-19 pandemic's impact on schooling, but under a significant broader context of multiple crises. Drawn from interviews conducted between October 2020 and February 2021, our data captures notions of “rupture” and “continuity,” underscoring amplifications in terms of systemic educational inequities. We focus on three key global narratives that emerged from the study, which when analyzed alongside insights from Lebanon, appear to be disconnected from how local actors experienced the pandemic. Our findings suggest that global narratives do not adequately account for the complexities of countries experiencing multiple crises, evoking questions around the capacity of international actors to understand and address multi-crisis environments in education. We discuss the implications of these findings for understanding and addressing power and equity in refugee education.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".