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Record W2007526490 · doi:10.1080/01436597.2015.1013319

The aid orphan myth

2015· article· en· W2007526490 on OpenAlexaff
Liam Swiss, Stephen Brown

Bibliographic record

VenueThird World Quarterly · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsMemorial University of Newfoundland
FundersEuropean Commission
KeywordsMetaphorPhenomenonDeveloping countryPolitical scienceOrphan drugMythologyDevelopment aidEconomic growthPublic relationsDevelopment economicsEconomicsEpistemologyHistory

Abstract

fetched live from OpenAlex

The term ‘aid orphan’ refers to a developing country forgotten or abandoned by the development community. This metaphor has featured prominently in the development assistance policy and research literature over the past decade. Development practitioners, policy makers and researchers have defined aid orphans in manifold ways and often expressed concern over the potential fate or impact of such countries. In this paper we first examine the many definitions of aid orphans and then review the main concerns raised about them. Next we empirically examine more than 40 years of bilateral aid data to identify aid orphan countries and their common characteristics. Our findings suggest that very few countries meet the definition of aid orphan and fewer still raise the concerns collectively expressed about the orphan phenomenon. We conclude by suggesting researchers and practitioners abandon the orphan metaphor and instead focus on issues of equitable aid allocation.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0060.020
Scholarly communication0.0070.015
Open science0.0010.009
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.306
Teacher spread0.275 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

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