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Record W7128500857 · doi:10.64903/1480-6800-21.4.331

Development Aid in Tumultuous Times: A Perspective from Muslim Geographies

2018· article· W7128500857 on OpenAlexaffvenue
M. Evren Tok, Cristina D’Alessandro

Bibliographic record

VenueArab world geographer · 2018
Typearticle
Language
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIslamDevelopment aidPerspective (graphical)TerrorismPovertyCorporate governanceRefugeeInternational developmentDual (grammatical number)

Abstract

fetched live from OpenAlex

Geographies of the Islamic world manifest stark disparities in terms of social and economic development. While some of the countries of the Ummah (Islamic community) benefited from Western donors and their development aid, there have been efforts to build endogenous capacity. A relatively well-known example is the case of the Organization for Islamic Cooperation (OIC), one of the major but not the only actor in Islamic development assistance. This study is an attempt to showcase Islamic development aid emanating from the Ummah, its geographical and organizational fractions and conditions. Islamic aid is intrinsically informal by nature and its informal functioning is a fundamental reason for its dual relation with their Western counterparts. Born to embody the Islamic alternative to Western multilateral development cooperation, Islamic and Western aid institutions are similar when it comes to sectoral priorities and to their geographical focus. Islamic aid is turning to sub-Saharan Africa, where the poorest and most vulnerable populations and some strategic priorities (like terrorist groups and the refugee crisis) for global governance are. Gulf countries are a peculiar and critical OIC sub-group: they are the larger donors within the OIC and also individually through bilateral cooperation.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.008
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2018
Admission routes2
Has abstractyes

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