A Regulatory Budget and a Strategic Regulatory Agenda: Twin Reforms for More Strategic, Integrated and Democratic Regulatory Governance
Bibliographic record
Abstract
I will begin by briefly reviewing regulatory budgets illustrated by the current activities in the UK, which is the first country to adopt such a reform. I will then describe the concept of a strategic regulatory agenda for the Government of Canada and demonstrate first how the two starting logics are different but also how closely entwined the two reforms are. I will conclude that regulatory budgets are not sufficient and, perhaps, not even primary. Instead, the two tools – budget and agenda – are twin missing links in achieving a more democratic and strategic integrated approach to regulatory governance. We need to ask whether one can have a strategic regulatory agenda without a regulatory budget to discipline it. We need equally to ask whether one can have a regulatory budget without a very open and public cross-governmental and political strategic agenda to democratize it.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".