Conflict and Crime in the Society: A Bane to Socio-Economic Development in Nigeria
Bibliographic record
Abstract
Just like conflict, crime is functional and pervasive as no unit of society is totally free and spared of it woes and throes when it occurs. The way the polity is organized can create both root causes and conditions favourable to negative conflict with its attendants consequences. Societies like Nigeria and other developing countries; where corruption and corrupt practices are heralded, politicians are above the law and the unfit are preferred in positions cannot possibly escape intractable violence and destructive conflict which arguably can undo economic, political and social gains of country and scare potential investors. Two theories (conflict and functional) were considered to explain the phenomena of conflict and crime. The findings reveals that the primary beneficiaries in conflict situations are politicians (they provide arms and fund conflictants); and poverty was seen as another reason for the meteoric rise of conflict and crime in Nigeria. They study made some recommendations among which includes; the existing legislations on conflict and crime as provided in the criminal code should be implemented and culprit punished and vexed issues should be amicably redress through the court, council of chiefs and elders as it is done in African traditional setting. Key words: Conflict; Crime; Threat; Insecurity destruction
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".