PEDAGOGY AND IMPACT OF GIFMIS ADOPTION AS A TOOL FOR PUBLIC FINANCE MANAGEMENT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it