Assessment practices in the policy and politics cycles: a contribution to reflexive governance for sustainable development?
Bibliographic record
Abstract
This article examines systematic assessment practices linked to sustainable development policies. We consider five types of assessment—monitoring, policy evaluation, formal audit, peer review, and specialist reporting—and explore their fate in the policy and electoral politics cycles. In contrast to traditional views of the policy cycle, we note that systematic assessments provide complementary feedback around the entire policy cycle. However, despite this omnipresence, their policy relevance is usually severely limited, inter alia because the policy cycle captures only parts of the political reality. A major concern for politicians (but not necessarily for policy or governance scholars) that goes far beyond the formulation and implementation of policies is the broader cycle of electoral politics that determines the state's political personnel as well as government priorities. Here, we highlight that the findings of systematic assessments are often lost in a cacophony of voices to which politicians are more carefully attuned, such as media responses and opinion polls, implying that scientific evidence is simply ‘overwritten’ with other kinds of evidence representing alternative rationalities and priorities. Despite numerous shortcomings, the true value of systematic assessment practices lies in their potential to furnish ammunition to state and non-state actors interested in securing change.
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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 | 0.283 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.091 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".