Bibliographic record
Abstract
Politicians routinely give regulators the authority to make decisions that affect the interests of others, but set procedures for the exercise of this authority. Procedures are said to guide the substance of the decisions regulators reach by making these decisions more transparent. The more sunlight its decisions attract, the less likely it is that a regulator will pursue its own policy preferences in place of those reflected in legislation. This account of how procedures guide substance has been the subject of persuasive criticism, however, which in turn casts doubt on whether procedures guide substance in the first place. If politicians cannot guide regulators’ decisions through procedures, this would seem to call into question the democratic pedigree of much of the administrative state. In Canada, securities regulators, with their broad powers to engage in rulemaking with little to no direct involvement from politicians, would seem especially vulnerable on this score. This thesis argues it is too early to reject the claim that procedures guide substance. Instead, it proposes an alternative account of how procedures might guide substance: by shaping the learning process that feeds into regulators’ decisions. By shaping regulators’ choices about what and how much information to gather and use in making decisions, procedures may serve to ensure these decisions reflect value judgments made by politicians and reflected in law. The thesis develops this account through three case studies, each comparing Canadian securities regulators’ efforts to learn about and respond to a new financial innovation against those of their US and UK counterparts, and illustrating how procedures might have helped guide them in different directions. If procedures guide substance, this does not necessarily mean that regulation always promotes social welfare or some other interpretation of the public interest. Procedures might inadvertently guide regulators in a different direction than politicians intended. Politicians might also use procedures to guide regulators towards making decisions that cater to vested interests. The thesis suggests pathways for future work shedding light on these risks and options for reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".