Fiscal Transparency and Citizen Trust: Evaluating the Role of Budget Accountability Mechanisms in Developing Democracies
Bibliographic record
Abstract
In developing democracies, the fiscal transparency and trust by the citizens are essential elements of proper governance.This paper will look at how the budget accountability (participatory budgeting, audit committees, citizen oversight, andopen data platforms) have influenced the building of the trust of the people. Based on cross-country information, casestudies on Nigeria, Uganda, Ghana and Kenya and emerging empirical studies, the analysis has established a strong positiverelationship between transparency indicators and trustworthiness of the citizen to the government institutions. Resultshave shown that systems that allow active citizen involvement and real-time availability of fiscal data make a remarkabledifference in increasing the level of trust, minimizing corruption risks, and enhancing perceived legitimacy in how thefinance is managed by the populace. The paper highlights the need to make fiscal transparency practices and institutionalreforms complementary so as to promote democratic accountability and sustainable development. The policy suggestionsto the policy makers are to increase citizen participation, embrace the use of online platforms to disclose fiscal details, andestablish routine and reliable reporting of budgetary performance.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| 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".