Policy Evaluation in the Era of COVID-19
Bibliographic record
Abstract
<P>Did evaluation meet the challenges of the COVID-19 crisis? How were evaluation practices, architectures, and values affected? <I>Policy Evaluation in the Era of COVID-19</I> is the first to offer a broad canvas that explores government responses and ideas to tackle the challenges that evaluation practice faces in preparing for the next global crisis. Practitioners and established academic experts in the field of policy evaluation present a sophisticated synthesis of institutional, national, and disciplinary perspectives, with insights drawn from developments in Australia, Canada and the UK, as well as the UN.</P><P>Contributors examine the impacts of evaluation on socioeconomic recovery planning, government innovations in pivoting internal operations to address the crisis, and the role of parliamentary and audit institutions during the pandemic. Chapters also example the Sustainable Development Goals, and the inadequacy of human rights-based approaches in evaluation, while examining the imperative proposed by some authors that it is time that we take seriously the call for substantial transformation.</P><P>Written in a clear and accessible style, <I>Policy Evaluation in the Era of COVID-19</I> offers a much-needed insight on the role evaluation played during this unique and critical juncture in history.</P>
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How this classification was reachedexpand
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 | 0.030 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; both teacher heads agree on what is shown here.
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".