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Record W4295921501 · doi:10.1093/oxrep/grac019

How do policy approaches affect refugee economic outcomes? Insights from studies of Syrian refugees in Jordan and Lebanon

2022· article· en· W4295921501 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford Review of Economic Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsRefugeeEconomic growthPolitical sciencePsychological interventionDevelopment economicsLivelihoodWork (physics)EconomicsMedicineGeography

Abstract

fetched live from OpenAlex

Abstract The vast majority of refugees globally are hosted in developing countries. In Jordan and Lebanon, nearly one in ten people are refugees. This paper reviews how different policy environments in Jordan and Lebanon have shaped economic outcomes for Syrian refugees, focusing on education, work, social assistance, and welfare outcomes. The review summarizes key research on how to improve refugee economic outcomes. We demonstrate that there can be effective service delivery for refugees, dependent on state capacity. For example, differences in policy led to better education outcomes for Syrian refugees in Jordan than in Lebanon. A variety of interventions can support refugee livelihoods, while generally doing no harm to host communities. Both countries also demonstrate the difficulties of achieving refugee economic self-sufficiency. Although Jordan has allowed (limited) legal work opportunities for refugees, Syrian refugees in both countries remain primarily in precarious work and supported by international aid.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.346
Teacher spread0.298 · 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