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Record W2765550381 · doi:10.1017/s0020743817000666

“PEOPLE EAT PEOPLE”: THE INFLUENCE OF SOCIOECONOMIC CONDITIONS ON EXPERIENCES OF DISPLACEMENT IN JORDAN

2017· article· en· W2765550381 on OpenAlexaff
Giulia El Dardiry

Bibliographic record

VenueInternational Journal Middle East Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeState (computer science)Socioeconomic statusGender studiesDisplacement (psychology)SalientSociologyArgument (complex analysis)Political sciencePsychologyLawMedicinePopulationDemography

Abstract

fetched live from OpenAlex

Abstract This article explores the ways in which refugee and host experiences of displacement in Jordan between 2010 and 2013 were articulated in a socioeconomic register that coincided with, but was also independent of, both state biopower and historical cross-border regionalisms. I argue that this register became salient due to a shared understanding of everyday life as characterized by what I term hunger , a state of depredation where “people eat people” to attain their own well-being. In pursuing this argument, the article has two goals: to show how Iraqis and Jordanians negotiated the complexities of living together in hunger by censuring individuals—locals and foreigners, rich and poor—who contributed to producing hunger rather than to alleviating it, and by consciously resisting the corrosive effects of hunger on social relations; and, more generally, to challenge universalizing understandings of refugee experiences according to which local tensions between refugees and hosts are derivative of a globalized antiforeigner discourse.

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)0.001
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 score0.322
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.362
Teacher spread0.319 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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