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Record W4376562097 · doi:10.1080/14662043.2023.2200598

Rwanda-Uganda relations: elites’ attitudes and perceptions in interstate relations

2023· article· en· W4376562097 on OpenAlexaff
Gerald Bareebe, Moses Khisa

Bibliographic record

VenueCommonwealth and Comparative Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsYork University
Fundersnot available
KeywordsIdeologyCovertEspionageResistance (ecology)LawPolitical scienceFront (military)Government (linguistics)ConstructiveSociologyInternational relationsAuthoritarianismPoliticsMedia studiesDemocracy

Abstract

fetched live from OpenAlex

Rwanda and Uganda have had strained relations, oscillating between warm, lukewarm, hostile and outright war. Since the biggest falling out during the Second Congo War (1998–2003), both governments have variously accused each other of wrongdoing, including allegations of supporting rebel activities, covert counterintelligence operations and espionage. The most recent escalation in frosty relations saw the closure of Katuna border post. Because the respective ruling parties – the Rwandan Patriotic Front and the National Resistance Movement – at a minimum have shared ideological and historical origins, we would expect relations to be strong and constructive not hostile or tenuous. Yet, it is precisely the shared history and social ties among the politico-military and intelligence elites that shape the suspicion, mistrust and hostility that feed into official policies. This article analyses how shared ideological and historical origins, social relations and kindred ties inform individual attitudes and perceptions of key elites toward each other’s government.

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)0.003
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.385
Teacher spread0.290 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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