The Fight for Language: An Exploration of the Nigerian State’s Response to Protest Groups in Southeastern Nigeria
Bibliographic record
Abstract
The resurgence of pro-Biafra movements led by the likes of MOSSOB and IPOB over the past six to seven years is proof of the failed accountability the Nigerian government has displayed in addressing the trauma of the Biafran war. Nigeria’s deliberate choice to ignore particular aspects of its history is a reflection of its refusal to adequately address the impact of state violence on its people. The labeling and crackdown on these groups, as extremists that pose a threat to Nigerian security, disregards the principle issues that allow for the existence of these groups in the first place. While understandings regarding the need for a Biafra to exist vary between the present and past, the underlying tension between the state and these secessionists groups remains the same: The protection of Nigerian security continues to be more important than the people who make up the state. In this study, I examine how the use of language, such as “extremists” or “terrorists” by the Nigerian government in labeling various protest groups and, particularly in the Southeastern region, functions as an attempt by the Nigerian government to maintain control over the area’s resources and invalidate the concerns of the peoples in those areas to the general public. This has resulted in the proliferation of further tension between state and minority groups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".