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Record W4392855721 · doi:10.32920/25412818.v1

Portrayals of the Tigray War in Western News Media; the Framing of Ethnic Conflict in the Globe and Mail and New York Times During 2020-2021

2024· preprint· en· W4392855721 on OpenAlexaff
Sara Esayas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsToronto Metropolitan UniversityCentre for Social InnovationYork UniversityUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)Ethnic groupPoliticsPolitical scienceEthnic conflictNews mediaDiasporaGlobeMedia studiesGender studiesSociologyGeographyLawPsychology

Abstract

fetched live from OpenAlex

This research paper offers an examination of Western print news media coverage of the ethnic-based regional conflict in the Tigray region of Ethiopia. The aim of this study is to survey the coverage of the onset of war and the representation of the relevant political actors. It begins with a historical contextualization of ethnic federalism in the nation. I examine how specific ethnic groups are portrayed in Western print news media coverage and if news media are reporting on the role of digital activists. I identify that the reporting of the onset of the crisis in Tigray relies strongly on attribution of responsibility and conflict frames. News media routinely focus on specific political actors rather than deeper ethnic tensions which shaped the country’s political system and the current conflict in Tigray. I also establish that North American news media do not adequately report the role of digital activism within Ethiopia and in the diaspora. Key words: Ethiopia; news media; digital activism; ethnic federalism; ethnic conflict; Tigray

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)0.001
metaresearch head score (Gemma)0.002
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 score0.990
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.323
Teacher spread0.273 · 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
Published2024
Admission routes1
Has abstractyes

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