MétaCan
Menu
Back to cohort
Record W2885817853 · doi:10.1093/afraf/ady032

Rwanda’s post-genocide foreign aid relations: Revisiting notions of exceptionalism

2018· article· en· W2885817853 on OpenAlexaffabout
Marie-Ève Desrosiers, Haley J. Swedlund

Bibliographic record

VenueAfrican Affairs · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsGlobal Affairs CanadaInternational Development Research Centre
Fundersnot available
KeywordsGenocideExceptionalismInternational relationsPolitical scienceMythologyGovernment (linguistics)SociologyLawHistoryPoliticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article studies donor–government relations in Rwanda since the end of the 1994 genocide. The notion that Rwanda enjoyed or enjoys exceptional relations with donors because of guilt regarding their inaction during the genocide is widespread in the literature and in policy circles. To assess this myth, the article first looks at aid trends for Rwanda and comparable countries, and then takes an in-depth look at aid relations with two average-size donors: Canada and the Netherlands. It demonstrates that Rwanda is not as exceptional as claimed, but instead should be considered one amongst a group of exceptional cooperation partners. The article further highlights that donors operated informally immediately following the genocide, but soon renormalized aid relations, and that there has always been a complex set of rationales determining donor behaviour regarding Rwanda.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.022
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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 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

Citations37
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAfrican AffairsSame topicInternational Development and AidFrench-language works237,207