Dealing with the challenges of legitimacy, values, and politics in policy advice
Bibliographic record
Abstract
Abstract Policy advice has been the subject of ongoing research in the policy sciences as it raises fundamental issues about what constitutes policy knowledge, expertise, and their effects on policymaking. This introduction reviews the existing literature on the subject and introduces the themes motivating the articles in the issue. It highlights the need to consider several key subjects in the topic in the contemporary era: namely the challenge of legitimacy, that of values, and the challenge of politics. The papers in the issue shed light on the ongoing delegitimization of conventional knowledge providers, the problem of the normative basis of experts’ advice, the increasing politicization of expertise in policymaking, and the relevance of political context in influencing not only the role of experts but also whether or not their advice is accepted and implemented. It is argued that these modern challenges, when not addressed, reinforce trends toward the inclusion of antidemocratic values and uninformed ideas in contemporary policymaking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.036 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".