The Nexus Between Good/Bad Governance and Citizens’ Participation in Nigeria’s Electoral Process
Bibliographic record
Abstract
The place of the state in deciding the lives of the citizenry cannot be negotiated. This is because the first responsibility of the state is to protect the lives and properties of the citizenry. Ironically, the ability of the state to protect the lives and property of its citizenry would be measured on the level of governance impartation. When governance is good, the people will enjoy good life and smile but when the governance is poor, the masses will suffer. This study focuses on an assessment of the place of good/ bad governance in determining the nature of the electoral process in Nigeria. The study adopted a qualitative approach and used secondary data from other literature sources. The findings revealed that Nigeria is overtaken by bad governance and that has given fillip to electoral malpractice at all levels of the country’s election process. The study further revealed that the implication of the nexus between bad governance and electoral malpractice in the country include increased mortality rate, poverty and all manners of nepotism. The study concluded that bad governance in Nigeria has undermined the citizen’s participation in the electoral process. It recommends strategic steps like decentralization of the Nigerian state as well as digital elections and digital transmission of electoral results as the paths towards Nigerian national transformations. The study contributes to the wider body of literature in the area of politics, governance and policy analysis. Keywords: Election, Good Governance, Citizens’ Participation, Nigeria
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".