Bibliographic record
Abstract
The problem of unregulated use of money in politics did not begin today.There are antecedents in the history of modern Nigeria, beginning with the politics of nationalism in the 1950s, similar to rent-seeking behaviours of parties, politicians and voters.For example, the absence of strict legislation to regulate party finance made it possible for politicians and political parties to engage in illegal party financing and corruption in the Nigeria's First Republic.The electoral laws under which elections were conducted in the 1950s and 1960swere derived from the provision of the British Representation of the Peoples Act of 1948/9 and its regulations.The 1959 elections were conducted under the provision of the Nigeria (Electoral Provisions) Order-in-Council, LN 117 of 1958 enacted by the British Parliament.During this period, there was no clearly defined regulatory framework on party finance and political party funding was primarily carried out through private parties since candidates were responsible Granted that some efforts have been made to reform laws regulating political campaigns and party funding, campaign financing and their abuses thereof remain shrouded in mystery.It is in this connection that this chapter critically interrogates the challenges of political parties and election/campaign financing in Nigeria, with specific emphasis on the 2015 general elections.The chapter demonstrates that despite the existence of an enabling Act to sanitize campaign financing in Nigeria, the suspicious manner in which the presidential candidates of the two major political parties mobilized huge campaign funds in the wake of the 2015 general elections, reveals not just the contempt with which they hold this law, but also exposes the political corruption and commercialization of the electioneering process.The methodology made extensive use of secondary sources and employed the technique of content analysis to analyse both descriptive, narrative, and empirical data on election expenses.The chapter also argues that the commercialization of the electioneering process does not only disempower and dispossess citizens during the post-election period, but has other far reaching-implications for the nation's democratic trajectory.The chapter concludes by positing that there is the need, not just to strengthen institutions but, to make them more proactive in the discharge of their statutory responsibilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.972 | 0.963 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".