The Challenges of Democratic Governance in Bayelsa State: Exploring Political Corruption
Bibliographic record
Abstract
Political corruption has continued to pose a hindrance to democratic governance in Bayelsa State since the birth of democracy in 1999 in the state. This study, “interrogating the interface between Political corruption and Democratic Governance in Bayelsa State”, examines the relationship between political corruption and democratic governance. Political corruption impedes the benefits of democratic governance, however one must first acquire political power before becoming politically, Corrupt. From the beginning of the first republic to date, democratic governance in the state (Bayelsa State) has not really given much to the people. As the State with the least number of Local Government Areas in Nigeria’s Federal system, the level of development is not commensurate with the amount of financial resources received including the 13% oil revenue it had received from the federation Account Allocation Committee (FAAC) of the federal government. This is not unconnected to a corrupt political class in the state. The first executive governor of the state was convicted of corruption, and two past governors of the state were entangled in corruption charges. What are the effects of political corruption? It is observed in this study that, infrastructural and human capital under development in the state are major outcomes of political corruption. The only way to do away with this class of politicians is through the ballot, therefore there should be serious sensitization of the people, championed by Civil Society Groups (CSOs) on the evils of political corruption, and the need for them to reject any financial or material gifts as inducements from the political class especially on the day of election.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".