Bibliographic record
Abstract
In March 2020, the World Health Organization declared the COVID-19 virus outbreak a pandemic.The COVID-19 pandemic rapidly became a global, national, provincial, and local public health and socioeconomic concern, confronting government and other policy decision-makers with concrete challenges for responding to the changing scenarios worldwide.Evaluation practitioners were not spared from these changed circumstances, requiring impromptu choices and modifications to practice.Policy Evaluation in the Era of COVID-19-a volume edited by Eliadis, Naidoo, and Rist published in 2023-aims to depict the challenges evaluation practitioners faced during and after the COVID-19 pandemic, specifically clarifying how the field of policy evaluation ought to be able to contribute to the analysis of rapidly envisioned and constantly changing policy responses, such as those implemented to respond to the pandemic.In this context, Eliadis, Naidoo, and Rist's edited volume offers a productive discussion of how evaluation practice within international development agencies and national government institutions largely fell short of the changing policy analysis needs of governmental decision-makers arising with the COVID-19 pandemic.The authors pronounce an unambiguous call for substantial and substantive evaluation transformation to ensure continued relevance.More concretely, the volume outlines conceptual, organizational, and methodological explanations for the pandemic's impact on policy evaluation practice and offers tangible suggestions to transform the evaluation discipline.The COVID-19 pandemic (2020-21) provides the backdrop and forum for international and country-level policy evaluators to raise significant questions about the present and future of evaluation.The first part of this review offers a description of the volume's discussions; the second part focuses on the contributions and opportunities arising from this volume.Conceptually, Furubo's, Pawson's, and Patton's chapters turn toward evaluation history, complexity theory, and systemic societal change to explain why and how policy evaluation was not optimally positioned to contribute to pandemic response analysis and to offer evaluation transformation suggestions.Furubo (Chapter 1) considers the history of evaluation as a social practice to suggest that, in the context of the COVID-19 pandemic, evaluation was limited and delimited by its past defining characteristics.Furubo argues that policymakers underutilized evaluation, partly because the problem was initially conceived narrowly as a virus primarily requiring technical public health measures rather than a complex socioeconomic and political issue requiring comprehensive intersectoral policy response tools.However, Furubo adds that underutilization also resulted from evaluation's historically predominant features-i.e.evaluations must be purposeful activities that provide
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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 | 0.012 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".