Conference Examines Complementary Roles of Public & Private Enforcement in Investor Recovery
Bibliographic record
Abstract
WHAT: A one-day conference, entitled Public & Private Securities Enforcement: Improving Recovery for Harmed Investors, hosted by Osgoode Hall Law School of York University and the Canadian Foundation for Advancement of Investor Rights (FAIR Canada).\nThe conference will bring together representatives of regulators, legal practitioners, academic experts (including four Osgoode professors) and investor-focused policy commentators to discuss the complementary roles of public enforcement by securities regulators and private enforcement through securities class actions and other means.\nThe keynote address at 8:40 a.m. by Monica Kowal, Vice Chair, Ontario Securities Commission, will be followed by seven panel discussions:• Relationship between Public Regulatory Enforcement and Private Securities Class Actions;• Investor Recovery Facilitated by Securities Regulators: An Analysis of the Canadian versus the SEC Experience;• Jurisdictional Issues involving Foreign Investors, Foreign Exchanges and Foreign Public Companies;• Harmed Investor, Speedy Recovery: Industry-Funded Investor Compensation Funds;• The Impact of Whistleblower Programs on Investor Recovery;• Achieving Efficient and Effective Settlements under the OSC’s New No-Contest Settlement Scheme;• Recovery for Investors through Ombudservices.\nAgenda and speakers’ list available online:http://issuu.com/osgooderesearch/docs/investor_recovery_conference_-_agen\nWHEN: Monday, October 26, 2015, 8:00 a.m. to 4:30 p.m.\nWHERE: Osgoode Professional Development Centre, 1 Dundas Street West, 26th Floor. (The program is eligible for 6 Substantive Hours towards the annual CPD requirement with the Law Society of Upper Canada.)\n-30-\nYork University is known for championing new ways of thinking that drive teaching and research excellence. Our 52,000 students receive the education they need to create big ideas that make an impact on the world. Meaningful and sometimes unexpected careers result from cross-discipline programming, innovative course design and diverse experiential learning opportunities. York students and graduates push limits, achieve goals and find solutions to the world’s most pressing social challenges, empowered by a strong community that opens minds. York U is an internationally recognized research university – our 11 faculties and 24 research centres have partnerships with 200+ leading universities worldwide.\nMedia Contact:Virginia Corner, Communications Manager, Osgoode Hall Law School of York University, 416-736-5820, vcorner@osgoode.yorku.ca
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".