The Challenges Facing Evidence-Based Policy Making in Canadian Agriculture
Bibliographic record
Abstract
Several governments in Canada have made commitments to adopting evidence-based policy development. Several obstacles to the adoption of this approach have been identified in the policy literature. However, this literature has lacked an economic perspective. This is unfortunate, since economics has produced the most fully developed normative theory of government policy in the social sciences and humanities. The main elements of this theory are the theory of market failure and the theory of non-market failure, and the integration of those two elements in what Charles Wolf called implementation analysis. The Austrian economics tradition also offers the implications of what is often called Hayek’s knowledge problem and the lessons learned from the economic calculation debate as contributions to the understanding of the challenges facing the application of evidence-based policy. The authors propose adding four economic elements to the current model of evidence-based policy development: (1) providing sufficient and convincing evidence that a market failure has occurred; (2) providing sufficient and convincing evidence that a non-market failure is unlikely to occur or if it does occur the damages from the non-market failure will be less serious than the harm resulting from the market failure; (3) an appreciation of the distributed and conflicted character of social knowledge; and (4) the technical challenges involved in constructing a social preference order. The authors illustrate the application of the economic approach to evidence-based policy with an example from rural land use policy in Ontario.
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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 | 0.216 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.039 | 0.012 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".