Bibliographic record
Abstract
Attempts by researchers and policy-makers to address the ‘wicked’ issues which pervade environmental policy usually revolve around attempting – or recommending – both more participatory and transparent, and more systematic and evidence-based, policy-making. Post-normal science (PNS), with its ‘extended peer community’, has emerged as one approach, whilst others focus on procedural reforms of the policy process, particularly on enhancing democratic decision-making. This paper applies a novel analytical framework to a primarily documentary analysis of three cases we argue are wicked—Canadian regulatory review of health products and food, European union (EU) environmental thematic strategies, and United Kingdom (UK) energy and climate change policy. It explores how various responses to wicked issues are implemented, through the ‘lenses’ of PNS and, more generally, ‘democratic and effective decision-making’. It finds such responses are often limited by practical and fundamental barriers relating to handling of uncertainty, issue framing, participation, power, politics, and attitude to evidence. We draw conclusions about future research on PNS, particularly the need to more clearly relate theory to different strands of literature on the evidence–policy-making relationship, and to continue empirical testing.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.081 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".