Political Thuggery and Democratic Dividends in Nigeria: An Empirical Study
Bibliographic record
Abstract
The paper is an empirical study on political thuggery and democratic dividends in Nigeria. Utilizing secondary and primary data, the study tested four hypothetical assumptions. Analysis done through chi-square (x2) statistical test reveals that political thuggery hinders public accountability of elective officers thus a bane to good governance in Nigeria; increases crime rate thus a threat to the achievement of democratic dividends and exercise of citizenship rights in Nigeria. The study also reveals that the rising level of poverty, unemployment and weak security agencies account for the thriving market for political thuggery and violence in Nigeria. The more elective offices are made attractive financially the more politicians employ thugs during elections and campaigns. On the basis of these findings, the study recommends among others that government should make elective offices less attractive financially and conceited government programme and policies should be aimed at solving unemployment, poverty and strengthening the capacity of security agencies. Government must consistently educate the masses/youths on the evil of political thuggery and violence. Politicians should be made to sign undertaking that they would not employ thugs in their electioneering campaigns and that they will operate within the acceptable norms governing electoral process in Nigeria. Any politician who disobeys this should be severely punished and bound from participating in future elections. Key words: Political thuggery; Financial attractiveness; Elective offices; Unemployment; Poverty; Security agencies; Democratic rights
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".