The Educational Response to Syrian Displacement: A Professionalizing Field in a Politicized Environment
Bibliographic record
Abstract
Now entering its seventh year, the Syrian conflict has displaced millions, driving one of the largest displacements in modern history. This research examines the evolving discourses and coordination mechanisms of the educational response to the conflict in the region. It argues that the educational response to the conflict is unprecedented in many ways, as a state-led but regionally-coordinated response that has bridged immediate humanitarian needs with multi-year development approaches to the sector. It points to extensive structuration of the field of Education in Emergencies (EiE) at the regional level, through the creation of the No Lost Generation (2013), the development of the Regional Refugee and Resilience Framework (2015), and the strategic shift to align the field towards access, quality and systems strengthening in the wake of the London Conference (2016). The paper then explores the unanticipated side effects of the professionalization of EiE. The research draws on world society theory to explain how the field of EiE has effectively framed education in countries affected by the conflict in a rights-based framework, but also resulted in decoupling of the professionalized discourses of education from the political realities on the ground.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.036 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".