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Record W3041450326 · doi:10.18192/potentia.v8i0.4434

Beneath the Veneer of Peacebuilding

2017· article· en· W3041450326 on OpenAlexafffundvenueabout
Erin E. Troy

Bibliographic record

VenuePotentia Journal of International Affairs · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsYork University
FundersUniversity of Toronto
KeywordsPeacebuildingGenocideContext (archaeology)Political scienceComplicityAction (physics)SociologyLawGeographyArchaeology

Abstract

fetched live from OpenAlex

This research turns a critical eye to peacebuilding in Rwanda, by revealing the negative outcomes of efforts undertaken by Paul Kagame’s regime. Evaluation of five key pillars of peacebuilding demonstrates that a veneer of peacebuilding has again put Rwanda on a dangerous trajectory towards civil war. Examining the role of international greenlighting as a causal factor of the Rwandan genocide offers a new framework through which to understand our own complicity and responsibility. This framework, in the current Rwandan context, underscores the importance of interrogating ongoing patterns of greenlighting in the post-conflict period, and how we continue to contribute to conflict in the Great Lakes Region of Africa. Middle powers like Canada bear an onus to generate innovative methods of peacebuilding assessment, in order to understand actual impact on the ground. This allows us to see beyond insincere peace work, and points us towards a place of taking action.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.021
Scholarly communication0.0070.007
Open science0.0010.008
Research integrity0.0010.004
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 head0.026
GPT teacher head0.316
Teacher spread0.291 · 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

Citations0
Published2017
Admission routes4
Has abstractyes

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