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Record W2901397259

Social Networks and Humanitarian Aid among Urban Syrian Refugees in Jordan

2017· dissertation· en· W2901397259 on OpenAlexfundno aff
Matthew Russell Stevens

Bibliographic record

VenueYorkSpace (York University) · 2017
Typedissertation
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersYork University
KeywordsRefugeeSyrian refugeesInterpersonal tiesMiddle EastPolitical scienceHumanitarian aidEconomic growthSocial network (sociolinguistics)Development economicsGeographySociologySocial scienceSocial media
DOInot available

Abstract

fetched live from OpenAlex

Strong social networks have been shown to correlate with improved economic outcomes and emotional wellbeing in urban refugee populations. In the Middle East and North Africa, social networks are based on a wide variety of relational identities that interconnect, suggesting an array of opportunities for community self-support. However, this research shows that Syrian refugees living in Irbid, Jordan, no longer actively turn to social networks for support. The financial and emotional strain of exile, and the failure of international aid agencies to support the maintenance of pre-existing social connections or the development of new ones, has led to the collapse of social networks among Syrian refugees in Jordan. Without the ability to forge new, strong ties in urban Jordan, Syrians also struggle to make bridging ties with the local and humanitarian communities. The result is social and spatial segregation, humanitarian programming which is poorly attuned to the needs of Syrians, and a reproduction of camp space and associated relations of power in the urban setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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