Evidence of democracy? The relationship between evidence-based policy and democratic government
Bibliographic record
Abstract
The preceding decade has witnessed the production of a substantial volume of scholarship dedicated to exploring the concept of evidence-based policy – the idea that ‘at the heart’ of properly developed public policy is ‘the best available evidence’. Concomitantly, ‘evidence’ has secured an unparalleled pride of place (at least, rhetorically) among policy-makers, who will often refer to the conclusions of ‘the best available evidence’ when seeking to justify particular decisions. However, a number of additional considerations have been identified as desirably or otherwise influencing policy-makers’ decisions, including fiscal and time constraints, personal experience, societal values, and short-term/long-term political strategy, to name a few. Indeed, arguably, policy-makers in contemporary democratic polities have no choice but to incorporate such considerations into their decision-making if they genuinely wish to produce a policy proposal that is both politically and publicly viable. Does such a situation problematically impede the meaningful realization of evidence-based policy? This essay offers some preliminary observations concerning certain tensions between the idea of evidence-based policy and the practice of democracy and briefly considers the degree to which those tensions might and should be resolved. Key words: Public policy, evidence-based, democracy, political equality, majority rule.
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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 | 0.173 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.022 |
| 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".