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Record W2736090439 · doi:10.1080/08039410.2017.1344728

Foreign Aid and National Ownership in Mali and Ghana

2017· article· en· W2736090439 on OpenAlexafffund
Stephen Brown

Bibliographic record

VenueForum for Development Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Ottawa
FundersUnited Nations University World Institute for Development Economics ResearchCentre for Global Cooperation ResearchStockholms UniversitetSocial Sciences and Humanities Research Council of CanadaUniversität Duisburg-EssenUniversity of Ottawa
KeywordsHierarchyIncentiveDeclarationBusinessAid effectivenessPrioritizationAction (physics)Economic growthPublic economicsDevelopment economicsPolitical scienceLaw and economicsDeveloping countryEconomicsMarket economyLaw

Abstract

fetched live from OpenAlex

This article examines the principle of ownership, the keystone of the 2005 Paris Declaration on Aid Effectiveness, and its application to the cases of Mali and Ghana. It argues that both countries are characterized by a high level of ownership in its formal sense, that is to say, both have developed their own development plans, rather than having them imposed from outside. However, substantively, ownership is severely hampered by the existence of multiple plans, with no clear hierarchy among them, and a similar lack of prioritization within plans, as well as serious deficiencies in translating those plans into action. These limitations to the concept of ownership are best understood, not due to a lack of capacity or a simple lack of will per se, but as a result of interests and incentives, notably to maximize donor funding. As a result, the impact of the Aid Effectiveness Agenda on ownership practices in Mali and Ghana has been far more in form than in substance.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.112
GPT teacher head0.380
Teacher spread0.268 · 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 designObservational
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

Citations33
Published2017
Admission routes2
Has abstractyes

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