Government’s failure to protect its citizens against nomadic herders’ aggression: a tacit permission for self-defense in Plateau State, Nigeria
Bibliographic record
Abstract
Purpose This paper aims to present the continuous Nigerian Government’s failure to protect the lives and property of its citizens against the incessant itinerant herders’ violence, despite its numerous programs in attempts to end the carnage. It sought also to examine the relationship between this government’s failure to meet its responsibility and the ineluctable self-defense mechanisms adopted by the people of Plateau State, Nigeria. Design/methodology/approach The research was both quantitative and qualitative. The study was conducted in four of the 17 Local Government Areas of the state: Bassa, Jos-south, Riyom and Barkin Ladi. A sample size of 400 was determined using Yamane Taro’s sampling size formula. Four hundred respondents were interviewed using a Google questionnaire (found at this link: https://forms.gle/tu96ZDwP85e8JsGu8). In this study, a total of seven key informant interviews and nine focus group discussions were conducted. Findings The finding revealed that most indigenous ethnic groups were dissatisfied with the government’s handling of the nomadic herders’ aggression. Therefore, 99.1% of Berom, 99.0% of Irigwe and 92.9% of other ethnicities argued that the government’s failure to protect them is a tacit permission for self-defense. On the contrary, 60.0% of the Fulani were satisfied with the government’s strategies in ending the aggression and 95.0% of them argued that the government’s failure to protect its citizens is not an implied permission for self-defense. It was also found that a relationship exists between the government’s lack of capacity to end the nomadic herders’ aggression and implied consent for self-defense in Plateau State, Nigeria. Originality/value This is a research paper that uses primary data. The findings are germane to ending the challenge of recurrent aggression of nomadic herders on other Nigerians. The study concludes that the government must live up to its responsibility of the protection of its citizens’ lives and property, failure to do so is an implicit permission to the citizens to defend themselves. It also recommended that the government should return displaced people to their communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".