Framing victimhood, making war: A linguistic historicizing of secessionist discourses
Bibliographic record
Abstract
Abstract As separatist yearnings resurge and gain traction in Nigeria, the agency of language and digitality in spreading dissident discourses has come under scrutiny. In this study, I investigate the linguistic-historical dimension of the Biafran movements, exploring the rhetorical frames by which the actors curate ethnic victimhood and sustain the secessionist struggle. Drawing on a corpus of memoiristic narrative of the Biafra war and digitally mediated discourses from a new Biafran movement – Indigenous People of Biafra (IPOB), I identify and discuss the central topoi of warspeak in both narratives across space and time. In this context, the notions of linguistic framing and atrocity propaganda are fruitfully integrated to analyse the range of rhetorical strategies for incentivizing the struggle and for animating its social capital. While both narratives draw on shared belongings, historical precedents, cultural frameworks, and atrocity stories for incitement, they vary in style and audience. I attribute the shifts to changes in actors’ demographics, discursive contexts, and Nigeria’s ethnopolitical cartographies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".