Building Better Policies : The Nuts \n and Bolts of Monitoring and Evaluation Systems
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
Governments around the world face \n ongoing pressures from citizens to provide more and better \n services, and to do this under a tight fiscal environment. \n This provides the context for government efforts to ensure \n their policies and programs are as effective, and as \n efficient, as possible. An emphasis on government \n performance has led a number of governments to create formal \n systems for monitoring and evaluating (M&E) their \n performance on a regular, planned, and systematic basis with \n the objective of improving it. The focus of this book is on \n these government M&E systems: what they comprise, how \n they are built and managed, and how they can be used to \n improve government performance. M&E systems focus on \n measuring the results produced by government its outputs, \n outcomes, and impacts. The M&E system may exist at the \n level of an individual agency, entire sector, or the \n government as a whole. M&E can provide unique \n information about the performance of government policies, \n programs, and projects at the national, sector, and \n sub-national levels. It can identify what works, what does \n not, and the reasons why. M&E also provides information \n about the performance of a government, of individual \n ministries and agencies, and of managers and their staff. \n This book endeavors to expand the frontiers of issues that \n have been researched and analyzed. However, there are still \n a number of issues that are still not understood well \n enough. This book presents case studies on several countries \n that have succeeded in achieving high levels of utilization \n of M&E information, including Australia, Canada, Chile, \n and Mexico.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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