POLICY DESIGN FOR LEGITIMACY: EXPERT KNOWLEDGE, CITIZENS, TIME AND INCLUSION IN THE UNITED KINGDOM’S BIOTECHNOLOGY SECTOR
Bibliographic record
Abstract
More than ever, policy designers need to take legitimacy deficits seriously. To do so, they increasingly involve citizens in policy design processes and draw from a wider range of expertise. Where should they stop in terms of inclusiveness to citizens and expertise and for how long should they allow citizens and experts to be persuasive? These are the questions addressed in this article. Policy design legitimacy, the article argues, can be related to variations in designers and politicians’ inclination to resort to output‐oriented (expertise‐based) versus input‐oriented (citizen‐centred) design processes. Input‐oriented processes have a higher potential in terms of legitimacy deficit reduction than output‐oriented processes, but they take longer, notably because they require the involvement of large numbers of people. In contrast, output‐oriented processes have a slightly lower legitimacy potential, but can produce it faster. These propositions are illustrated by two policy design narratives drawn from the United Kingdom’s biotechnology sector.
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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 | 0.039 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".