From Studies to Systems: Ray Rist’s Influence on Evaluation Systems: Insights from International Research Group for Policy and Program Evaluation (INTEVAL)
Bibliographic record
Abstract
Background: Evaluation systems aim to embed evaluation as a standard and routine practice within public organizations. By integrating evaluation into the everyday operations of government, these systems have the potential to enhance the relevance and use of evaluation findings, support organizational learning, and contribute to more transparent and accountable governance. Although evaluation systems are often promoted as tools for strengthening the connection between evaluative knowledge and decision-making, it is important to examine how these systems may both facilitate and constrain the development and meaningful use of evaluation. Purpose: This article seeks to draw on the extensive body of knowledge developed by Ray Rist and the members of INTEVAL to better understand how evaluation systems are built, how they function, and what impacts they may have on the evaluation practice. By revisiting and synthesizing this extensive body of knowledge, we aim to extract key lessons for the design and implementation of evaluation systems that are both effective and adaptable to various contexts. Setting: As researchers and evaluators working within academic institutions, our work is informed by interdisciplinary and international perspectives that we use to examine the development of evaluation systems across diverse political and administrative contexts. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: This article is based on a literature review focusing on the institutionalization of evaluation and the development of evaluation systems. Particular attention is given to the Comparative Policy Evaluation series established by Ray Rist, which offers an interdisciplinary and internationally comparative body of work spanning over three decades. Drawing on this series, we examined how evaluation systems have been conceptualized, implemented, and critiqued over time and across diverse national settings. Findings: This article highlights that evaluation systems are shaped by contextual factors, institutional arrangements, and the contributions of key individuals. The effectiveness of these evaluation systems depends not only on organizational design but also on the capacity and engagement of evaluators, commissioners, and decision makers. The effective functioning of such systems fosters an environment favorable to evaluation, achieves a balance between supply and demand, and safeguards the independence and integrity of evaluation.
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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 | 0.237 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.013 | 0.088 |
| Scholarly communication | 0.037 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".