Party Primaries and the Quest for Accountability in Governance in Nigeria
Bibliographic record
Abstract
Modern democracy highlights the importance of political parties both in agenda-setting and in displaying party aspirants from whom the electorates must choose. This paper examines the processes of candidate selection against the backdrop of demand for accountability from the political officeholders in Nigeria. Interestingly representative democracy builds on the theory that the citizens are in control of the process through which their representatives are elected but empirical evidence suggests diversities in the selection process. Nigeria has experienced about twenty-two years of uninterrupted democratic rule but each successive electoral period highlights a display of citizen’s discontentment with their representatives. This phenomenon raises a fundamental question about how their representatives were ab-ini-tio selected. There has been a paucity of research on how the conduct of party primaries set the contour for the people-oriented governance in Nigeria. This paper examines a candidate’s selection within parties and its implication for accountability. It argues that the structure of party primaries in Nigeria cannot but empower party bigwigs to impose aspirants that will undermine engendering accountability in governance. It argues for strong institutional mechanisms and civil society’s role to prevent elected representatives from doing the bidding of their godfathers.
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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 | 0.004 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 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".