Policy Assessment in the OECD : Lessons for Chile
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
Chile is well-advanced in the field of \n program and project evaluation, with adequate institutions \n and procedures in place, and has achieved a very high \n standard by any international comparison. DIPRES has \n established a system of evaluations of sound quality. This \n system promotes the utilization of evaluation results in \n management decisions, including budget decision. The \n outsourcing of evaluations guarantees technical and \n political independence of program and project evaluations, \n while increasing their credibility. On the other hand, \n policy evaluation in Chile is mainly an ad-hoc and \n spontaneous activity, with no definite procedures or \n standards. Regardless of the quality of those sporadic \n evaluations, the fact remains that no one is responsible for \n the selection, methods, implementation, financing, and \n utilization of the results of policy evaluations. This \n report will focus on developing a strategy and instruments \n for further institutionalizing public policy assessment in \n Chile. The first chapter discusses definitions and concepts \n related to the public policy process and describes the scope \n of this report. Chapter second examines the policy processes \n of six Organization for Economic Cooperation and Development \n (OECD) countries, including federal countries such as Canada \n and the United States (U.S.) and unitary countries similar \n to Chile. Chapter third takes the OECD context as background \n to analyze Chile’s own policy process and lays out \n challenges to improving the policy process in Chile. Chapter \n fourth builds on the previous analysis to offer a number of \n possible directions Chile can take to achieve its goal of \n strengthening public policy assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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