Social media discourses amidst ethnopolitical extremism and conflict: The case of Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines how influencers and political armies in Ethiopia use social media specifically Facebook to propagate sectarian rhetoric and ethnopolitical extremism within a society grappling with protracted internal conflicts, prolonged ethnopolitical extremism, and armed clashes. The study employs qualitative research methodology, using critical discourse analysis (CDA) alongside facets of rhetorical analysis, to examine Facebook posts and comments produced in English and Amharic languages during the post-Tigray War politically tense period (the final quadrimester of 2023) amidst armed conflicts in the Amhara region. By purposively sampling content from pages of political influencers and elites, the study investigates the rhetorical patterns and discursive constructions shaping ethnopolitical narratives. The analysis reveals that the rhetorical and discursive patterns and textual trajectories on Facebook in Ethiopia are characterized by hostilities and animus, which are prone to promoting ethnopolitical radicalization and deepening the schism among ethnic and political groups.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it