Public Expectations from Political Office Holders on Good Governance in Oyo State, Nigeria
Bibliographic record
Abstract
This study examines the expectations of the electorates from political office holders, using Oyo state as a case study. It also finds out how feasible the expectations are, and if political office holders are able to meet such expectations. The study equally examines the factors that aid and prevent such expectations from being met and discusses the impact of the expectations on good governance in the study area. Primary data was sourced with a self structured questionnaire administered on 150 respondents drawn from electorates, public officials/elected officials and civil society organizations across the 33 local government areas of Oyo State, analyzed by Statistical Package for Social Scientist (SPSS) and interpreted in percentages and frequency distribution. The secondary data was sourced from books, journals, newspapers publications and internet materials, and content analyzed. The study revealed that people have varied expectations but same on medical facilities, poverty alleviation, security of lives and property, education and economic development. The study also revealed that political office holders are aware of the expectations of the people through the mass media, social media and direct contact but do not meet them. The study found that adequate fund and manpower are key factors that help political office holders to meet up with the expectations and that lack of funds; inadequate manpower, corruption, and influence of political godfathers are hindrances to meeting up with the expectations of the electorates. It also found that public expectations have impact on good governance and that the impact is a positive one.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".