Development Aid in Tumultuous Times: A Perspective from Muslim Geographies
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".