Bibliographic record
Abstract
The objective of this paper is to examine the economic cost of Fulani-Farmers Clashes on the populace in general and the nation's economy in particular.This is because insecurity and its various multifaceted manifestations like bombings, cattle rustling, farmland destruction, kidnapping/hostage taking, destruction of life and property, creation of fear among others has become a hydra headed monster which security agents in Nigeria appear incapable of addressing.Bloody clashes between Fulani herdsmen and farmers over grazing lands have led to the killing or maiming of people and razing down of houses as well as food storage facilities.The herdsmen claimed that they are the original owners of the land in the agrarian areas.According to them, the natives had sold it to them for their cows to graze.This is an allegation the farmers have consistently debunked, saying that the land was never at any instance sold to the herdsmen and that the cows damage their crops while grazing.Cattle-rustling has also been a major cause of unrest as cows are stolen by criminal-minded youths.This scenario has played out many times in Guma, Makurdi, Gwer West, Agatu, Logo, Kwande, Buruku and parts of Kastina-Ala local government areas of Benue State.The same is common in Enugu, Delta, Taraba and Plateau states.This paper takes a look the economic effects of these conflicts by identifying the remote causes and possible solutions to the challenge.The theory of Human needs served as our framework of analysis while documentary methods of analysis and content analysis were used to generate and analyze data.The study revealed that this pattern of insecurity challenge is detrimental to general well being of the people with its resultant effects in the area low quality of life, food insecurity, high cost of food, population displacement and even death, the destruction of business, properties and equipments, relocation and closing down of businesses.The study suggests that the Nigerian government and her security agencies should be pro-active in their responses, improve their intelligence gathering techniques and peace building and equip and motivate her security forces better.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.965 | 0.965 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".