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Record W3033325180 · doi:10.25071/1920-7336.40623

Extractive Landscapes: The Case of the Jordan Refugee Compact

2020· article· en· W3033325180 on OpenAlexvenueno aff
Julia Morris

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeEnforcementPolitical scienceValue (mathematics)Development economicsImmigrationEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

In a climate of immigration securitization, recent years have seen a global move away from humanitarian resettlement responses in sites of displacement. Instead, wealthy governments in the Global North often finance poorer third countries and rural regions of territories to abet border enforcement. The Jordan Compact, in particular, has been upheld as an economic development model that provides an “innovative alternative” to refugee camps, as well as to protracted refugee situations. Yet, as much research shows, the direct economic gains from this trade concessions scheme have been limited. This raises the question, What value does the Jordan Compact hold with such ample evidence of failure? Importantly, how is this failure experienced by refugees in practice? Drawing on fieldwork conducted in Amman and northern Jordan, this article advances a framework centred on extractivism to better detail how value is extracted from migrants and displaced persons at the expense of their well-being. The article illuminates the disjuncture between the lack of profit achieved directly from the Jordan Compact’s trade concessions and the forms of value extracted from refugees’ immobility. Overall, I argue that these economic development policies formalize precariousness, allowing the international community to abdicate global responsibility and reap the benefits of a purported altruism.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0320.022
Scholarly communication0.0070.005
Open science0.0020.012
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.287
Teacher spread0.266 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicAsian Geopolitics and EthnographyFrench-language works237,207