An Overview of Public Sector Budget Monitoring & Evaluation Systems for Gender Equality: Lessons from Uganda and Rwanda
Bibliographic record
Abstract
Citizen expectations regarding government accountability and transparency are rising around the globe and this has given politicians and public administrators an obligation to account for their actions more regularly than in the past. Although a number of African countries have made notable strides in public expenditure management, citizens` level of trust in government is eroding owing to administrative challenges such as corruption, embezzlement of public funds and ineffective delivery of public services. Against this backdrop, public sector budget monitoring and evaluation has emerged to spur efficiency, effectiveness and transparency within organisations and institutions in relation to meeting developmental goals and outcomes. One of the socio-economic ills prevalent in Africa is the failure to channel resources towards the achievement of gender outcomes as shown by existing gender disparities. Using desktop research, this article responds to this ultimate concern by examining the extent to which Uganda and Rwanda have played a leading role in the implementation of budget M&E to achieve specific gender outcomes. Results show that although a number of countries have transformed their budget monitoring and evaluation mechanisms, only a few have managed to align these systems to gender equality goals.
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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 | 0.049 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".