An ethical analysis of policy dialogues
Bibliographic record
Abstract
BACKGROUND: A policy dialogue is a tool which promotes evidence-informed policy-making. It involves deliberation about a high-priority issue, informed by a synthesis of the best-available evidence, where potential policy interventions are discussed by stakeholders. We offer an ethical analysis of policy dialogues - an argument about how policy dialogues ought to be conceived and executed - to guide those organizing and participating in policy dialogues. Our analysis focuses on the deliberative dialogues themselves, rather than ethical issues in the broader policy context within which they are situated. METHODS: We conduct a philosophical conceptual analysis of policy dialogues, informed by a formal and an interpretative literature review. RESULTS: We identify the objectives of policy dialogues, and consider the procedural and substantive values that should govern them. As knowledge translation tools, the chief objective of policy dialogues is to ensure that prospective evidence-informed health policies are appropriate for and likely to support evidence-informed decision-making in a particular context. We identify five core characteristics which serve this objective: policy dialogues are (i) focused on a high-priority issue, (ii) evidence-informed, (iii) deliberative, (iv) participatory and (v) action-oriented. In contrast to dominant ethical frameworks for policy-making, we argue that transparency and accountability are not central procedural values for policy dialogues, as they are liable to inhibit the open deliberation that is necessary for successful policy dialogues. Instead, policy dialogues are legitimate insofar as they pursue the objectives and embody the core characteristics identified above. Finally, we argue that good policy dialogues need to actively consider a range of substantive values other than health benefit and equity. CONCLUSIONS: Policy dialogues should recognize the limits of effectiveness as a guiding value for policy-making, and operate with an expansive conception of successful outcomes. We offer a set of questions to support those organizing and participating in policy dialogues.
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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 | 0.159 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.084 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".