A Discipline in Search of Itself: Contemporary Challenges for Securities Law in Canada - 2013 Ivan C. Rand Memorial Lecture
Bibliographic record
Abstract
Thank you very much to the University of New Brunswick's Faculty of Law for the invitation to deliver this lecture, and for the warm hospitality I received. I am honoured to deliver this lecture named in memory of Ivan C Rand. I know Justice Rand had a varied and brilliant legal career, having been an Attorney General, a Supreme Court judge, and dean of law. For these reasons I hope he would have sympathized with the enterprise I embark on this evening. That enterprise is to reflect on the disconnect between the way securities law is taught in Canadian law schools and the evolving practice of securities regulation itself. I have taught securities law at Osgoode Hall Law School for 15 years and recently became a practising regulator. I find it puzzling that what I teach as part of the core securities curriculum bears relatively little relationship to the questions preoccupying regulators in real time. Throughout this talk I want to explore the nature of this disconnect and how we might begin to correct 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".