Democracy, Governance, Legislative Challenges and Impediments in Nigeria, 1999 - 2011
Bibliographic record
Abstract
The concepts of democracy and governance have come to occupy a more “prominent position in the discourse of national and international development”. They have become key notions in which “citizens can have a more active role” which should enhance their positive view of the “increase legitimacy of the state”. As a matter of fact, both concepts are inseparable from the existential characteristics of all political animals all over the world. They continue to determine the “democratic political arrangement” and, dictate the landscapes and operational nerves of all civilized democratic polities in today’s ICT-driven world particularly in terms of the functional performance of the horizontal organs of government which have increasingly gained prominence vis-à-vis public policy and responsive service delivery.One of the critical components/units of these horizontal organs is the Legislature. The Legislature constitutes a cardinal part of the major pillars of the governance process in the democratic political systems of today’s world, Nigeria inclusive. Thus, this paper generally examines the challenges and impediments faced in Nigeria by the legislature over the years and concludes with the prescriptions for better legislative political terrains and the steps to be taken to make the Legislature in Nigeria more relevant to the sustenance of the ingredients and values of democracy and good governance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".