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Record W4379107353 · doi:10.1177/15423166231179235

Reconstruction and Resilience in Rwandan Education Programming: A News Media Review

2023· article· en· W4379107353 on OpenAlexfundno aff
Brandon Dickson

Bibliographic record

VenueJournal of Peacebuilding & Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)Political sciencePublic relationsContext (archaeology)Corporate governanceResilience (materials science)News mediaMedia literacyMedia studiesSociologyGeographyBusiness

Abstract

fetched live from OpenAlex

This study evaluates news portrayals of resilience in the newly renewed Education for Sustainable Peace in Rwanda initiative. There remains a gap in understanding about how Rwanda's education approaches are portrayed and disseminated to the public by news media. This research uses an inductive coding analysis and layers Entman's framing theory to evaluate new media portrayals of resiliency in the reporting of domestic and international media outlets. This research demonstrates that there has been little media attention paid to Rwanda's education system outside of Rwanda, despite the newly revised programming. This research also finds that in media sources which do discuss Rwanda's education system, portrayals of approaches to resilience that have the potential for inclusivity are far more common than approaches which are top-down and exclusive. These findings serve to contribute to literature in both the context of Rwanda's place in global governance and the broader discussions of educational resiliency.

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)0.005
metaresearch head score (Gemma)0.026
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.012
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.002
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 head0.032
GPT teacher head0.344
Teacher spread0.312 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Peacebuilding & DevelopmentSame topicPeace and Human Rights EducationFrench-language works237,207