An Overview of Public Sector Budget Monitoring & Evaluation Systems for Gender Equality: Lessons from Uganda and Rwanda
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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