Bibliographic record
Abstract
Since 1999 when democratic norms became the principle of state policy, delayed passage of Nigeria's annual national budget has become unduly too frequent.The timely passage of the 2013 budget seemed to have broken the jinx of lateness.The objective of this paper is to examine the impacts of the delays and their implications for Nigeria's budgetary process.This is because after months of delays, debates, shocking discoveries of alleged 'padding' and correction of omissions, the National Assembly has passed and the Presidency signed the 2016 Appropriation Bill totaling N6.06 trillion.The amount is, however, N17 billion less than the N6.07 trn proposed by President Muhammadu Buhari to the joint session of the National Assembly.In order to achieve the objective of the study, the paper generated data mainly from existing literature on budgets from books, journals, internet sources and draft budgets and other newspapers and magazines.The technique of Content Analysis was used to draw insights from the literature on areas that are considered very significant to the research.Drawing from these descriptive sources, this paper showed that given that a budget is both political and economic document that drive national politics and competitiveness.The paper concludes by suggesting the importance of transparency and accountability, as well as a legal framework that monitors, denounces, sanctions and punishes abuse in the preparation, use of public funds, would be crucial if success is to be attained in Nigeria's budgetary process.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.957 | 0.947 |
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".