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Record W4379529105 · doi:10.1002/sea2.12283

States of <i>faḍl</i> or stating <i>faḍl</i>: On the value of indebtedness for Iraqi exiles in Jordan

2023· article· en· W4379529105 on OpenAlexafffund
Abdulla Majeed

Bibliographic record

VenueEconomic Anthropology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsSocial Sciences and Humanities Research Council
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsCitizenshipSolidarityBureaucracyValue (mathematics)HospitalityAmbivalenceEthnographyReciprocity (cultural anthropology)Political scienceSociologyLawPolitical economyPoliticsSocial scienceAnthropology

Abstract

fetched live from OpenAlex

ABSTRACT A condition of excess characterizes Iraqi exiles' everyday life in Jordan: excesses of waiting and anticipation, bureaucratic work, and aspirations for future benevolent governance. To grapple with this excess, they have had to develop strategies that render their lives in exile more manageable. Despite being hosted as “guests” of the Hashemite monarchy—an ambitious status evoking notions of pan‐Arab solidarity and Arab traditions of hospitality—this status does not guarantee or grant them access to substantive citizenship rights. In light of this, Iraqi exiles who arrived in Jordan following the US‐led invasion of Iraq in 2003 have often found themselves dependent on potentially injurious ways to navigate their presence. One of these strategies are relations and practices of faḍl , a form of exchange governed by a foreclosure of reciprocity and necessity of public recognition. Based on ethnographic fieldwork among what I refer to as the Iraqi exilic milieu in Jordan, this article examines how, in the absence and denial of expected forms of exchange, the circulation of stately faḍl and its cooptation by ordinary people articulate new notions and practices of valuable yet nevertheless wounding citizenship.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.999

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 studies0.0000.002
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 head0.060
GPT teacher head0.356
Teacher spread0.296 · 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 designTheoretical or conceptual
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

Citations2
Published2023
Admission routes2
Has abstractyes

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