Expertise, policy advice, and policy advisory systems in an open, participatory, and populist era: New challenges to research and practice
Bibliographic record
Abstract
Abstract This article examines the themes of policy advice, expertise, and policy advisory systems. It argues that persistent challenges and more emergent trends involving their intersection can be effectively understood through the lenses of instrumentality, authority, and adaptability. In the wake of renewed questions about the continued viability of longstanding public administration advisory arrangements, these themes help locate new pressures on those arrangement such as those linked to technological developments, shifting conceptions of expertise, and growing recognition of the challenges of managing systems of advice. These themes help facilitate continued engagement with persistent challenges linked to adequate policy capacity, the role of the public service advice, and question of rigour, legitimacy, and the democratic contexts of policy advising. Points for practitioners Technological innovations and turbulent governance arrangements have renewed debates around technocracy, democratic control and participation, the role of evidence, and normative and ethical considerations inherent in the generation and use of policy advice. Policy capacity remains important for well‐functioning policy advisory systems. It has itself become multifaceted reflecting not only important differences in types of expertise and policy advice, but also concerns around its management and deployment in varying governance contexts. The competencies required for policy workers inside and outside of government should reflect changes in the role of expertise and evolving systems of policy advice.
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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 | 0.178 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.019 | 0.146 |
| Scholarly communication | 0.040 | 0.067 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 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".