The limits of policy analytical capacity: Canadian financial regulatory reform
Bibliographic record
Abstract
Purpose The purpose of this paper is to document Canadian financial services regulatory reform, illustrating how existing institutional fragmentation has undermined capacity to effectively learn from the global financial crisis. Design/methodology/approach The paper draws on government documents, public comments and interviews with key individuals in the reform process to document the different institutional responses to the crisis. Findings The case highlights concerns raised in the policy subsystems literature that the capacity for “learning”, even from events as significant as the financial crisis, can be undermined by poor institutional arrangements which create incentives for policymakers to support self interested and limited analysis. Practical implications The case suggests that effective learning in a complex sector requires that there be a national regulator charged with broad independent analytical responsibilities to ensure that the industry is effectively regulated. Existing fragmentation of responsibilities, combined with the interests of the industry and the current government in deflecting new regulatory rules, has meant that existing government expertise has not been effectively deployed. Originality/value The paper offers an important corrective to the existing view of Canadian financial regulation and is a compelling illustration of how poor institutional arrangements and ambiguous jurisdictional responsibilities can impede effective policy capacity in relation to learning.
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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 | 0.039 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".