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Record W2138650818

The politics of aid African strategies for dealing with donors

2009· preprint· en· W2138650818 on OpenAlexaboutno aff
Lindsay Whitfield

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsSovereigntyTanzaniaPoliticsNegotiationAid effectivenessPolitical sciencePower (physics)Development economicsDevelopment aidEconomic growthPolitical economySociologyEconomicsDeveloping countrySocioeconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

This book presents an original approach to understanding the relationship between official aid agencies and aid-receiving African governments. The first part provides a challenge to the hazy official claims of aid donors that they have stopped trying to force African governments to do what 'we' think is best for 'them' and instead are now promoting African 'ownership' of the policies and projects which foreign aid supports. The authors tease out the multiple meanings of the term 'ownership', demonstrating why it became popular when it did, but also the limits to this discourse of ownership observed in aid practices. The authors set out to defend a particular vision of ownership-one that involves African governments taking back control of their development policies and priorities. Based largely on interviews with the people who do the negotiating on both sides of the aid relationship, the country case studies put the rhetoric of the new aid system to a more practical test. The authors ask how donors seek to achieve their policy objectives without being seen to push too hard, what preconditions they place on transferring authority to African governments, and what effect the constant discussions over development policy have on state institutions, democracy and political culture in recipient countries. It investigates the strategies that African states have adopted to advance their objectives in aid negotiations and how successful their efforts have been. Comparing the country experiences, it points out the conditions accounting for the varying success of eight African countries: Botswana, Ethiopia, Ghana, Mali, Mozambique, Rwanda, Tanzania and Zambia. It concludes by asking whether the conditions African countries face in aid negotiations are changing. Contributors to this volume - Isaline Bergamaschi is a doctoral candidate in Politics and International Relations at Institut d'Etudes Politiques in Paris (Sciences-Po). Rachel Hayman is an ESRC post-doctoral fellow at the School of Social and Political Studies, University of Edinburgh. Alastair Fraser is a doctoral candidate at the Department of Politics and International Relations, University of Oxford. Xavier Furtado is with the Canadian International Development Agency (CIDA). Joe Hanlon is a Senior Lecturer in Development Policy and Practice at the Open University, UK. Graham Harrison is Reader in Politics and Director of the Political Economy Research Centre at the University of Sheffield, UK. Duncan Holtom is a Senior Researcher at the People and Work Unit, a voluntary sector organization based in the UK. Emily Jones is Trade Policy Adviser for Oxfam GB where she leads advocacy work on regional and bilateral trade agreements. Gervase Maipose is an Associate Professor and currently Head of the Department of Political and Administrative Studies at the University of Botswana. Sarah Mulley is coordinator of the UK Aid Network, a coalition of NGOs advocating for more and better aid. Paolo de Renzio is a doctoral candidate in the Department of Politics and International Relations, University of Oxford, and a Research Associate of the Centre for Aid and Public Expenditure at the Overseas Development Institute. James Smith is retired following a 24-year career at the World Bank dealing with aid and poverty issues, where his last position was Lead Economist for Poverty Reduction and Economic Management in Africa. Lindsay Whitfield was a Research Fellow at the Global Economic Governance Programme (2005-2008), and is currently a Research Fellow at the Danish Institute for International Studies, Copenhagen, Denmark.

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)0.019
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.027
Scholarly communication0.0190.012
Open science0.0010.012
Research integrity0.0060.010
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 head0.042
GPT teacher head0.357
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations410
Published2009
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicInternational Development and AidFrench-language works237,207