Transparency and Accountability in Public Financial Management: A Stewardship Account at Kano State Ministry of Finance, Nigeria
Bibliographic record
Abstract
This paper aims at accounting for the transparency and accountability demonstrated through the unity and togetherness of the functionaries of the Ministry of Finance Kano State, Nigeria as they collectively discharge the five key responsibilities of the Ministry during a period of 28 months (June 2015 to October 2017) under the humble leadership of the author. The paper is skewed towards practical (as against theoretical) dispensation of effective public finance management at the sub-national level in Nigeria, covering revenue management, expenditure management, debt management, investment management and wealth creation and management. This exposure of transparency and accountability has attracted some encouraging comments from hundreds of concerned analysists and lovers of transparency and accountability from different parts of the world, out of which a select sampled comments have been captured in the paper. The paper also attempts some responses to the seven questions raised by one of the commentators (An International Public Financial Management Expert) in respect of the earlier internet posting made on the subject matter. The paper recommends that research students, scholars and practitioners should conduct further studies on the issues raised in the paper so as to find solutions to the weak nature of the practice of prudence, transparency and accountability in the public sector of developing economies, like Nigeria.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".