Bibliographic record
Abstract
Updating rules to reflect new information about the world is easier said than done. Common approaches include providing for periodic review of legislation by the legislature and periodic review of regulation by a regulator. But Ontario securities law does something different. It calls for a full-scale review of securities legislation and regulation every four years by a committee of third-party experts appointed by the Minister responsible for administering securities law. This article takes a hard look at this process, which has generated significant controversy within the securities industry over the past year. Advisory committee members bring expertise to their roles and, unlike the government’s in-house experts (civil servants), presumably have no incentive to lean towards making recommendations that expand bureaucratic power. But it appears these third-party experts bring other incentives to the table—incentives that could impair the quality of their recommendations and subsequent legislative and regulatory change. This article identifies these potential incentives and proposes reforms that could mitigate the risks they pose. More broadly, the article serves as a case study illustrating the need to exercise care when outsourcing regulatory renewal to third-party experts.
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 | 0.120 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.015 | 0.009 |
| 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".