PEDAGOGY AND IMPACT OF GIFMIS ADOPTION AS A TOOL FOR PUBLIC FINANCE MANAGEMENT
Bibliographic record
Abstract
One major problem affecting economic growth of Nigeria is the poor management of the Nations Financial Resources. This arose from corruption, mismanagement and ill-allocation of government financial resources. The need to promote public accountability, transparency, cost effective public service delivery, judicious allocation of government scarce financial resources and economic growth gave impetuous for the introduction of Government integrated financial and management information system (GIFMIS). The study shall examine the effect of GIFMIS on government financial transactions in relation to public funds management and how it has significantly influence government policy. The paper adopts a survey design and primary data which were obtained with the use of well structured administered questionnaires. The data obtained were analyzed using an Analysis of variance (ANOVA). The findings reveal that with the use of GIFMIS, there has been an appreciable reduction in corruption, financial irregularities and leakages with the attendant improvement in transparency and accountability in the management of government funds. Also, the use of GIFMIS has led to effective implementation of government policy. The paper recommends the adoption of GIFMIS at all levels of government to form part of financial management reforms practices to enhance transparency, accountability and judicious use of government financial resources.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".