MétaCan
Menu
Back to cohort
Record W2925203293

The Educational Response to Syrian Displacement: A Professionalizing Field in a Politicized Environment

2019· article· en· W2925203293 on OpenAlexaff
Elizabeth Buckner, Mozynah Nofal

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProfessionalizationField (mathematics)Political scienceSociologyForced migrationPoliticsRefugeePolitical economyPublic relationsPublic administrationSocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.335
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207