Bibliographic record
Abstract
There has been significant academic buzz about Silver v. Imax, an Ontario case certifying a global class of shareholders alleging statutory and common law misrepresentation in connection with a secondary market distribution of shares. Although global class actions on a more limited scale have been certified in Canada prior to Imax, it can now be said that global classes have "officially" arrived in Canada. Many predict that the Imax decision means that Ontario will become the new center for the resolution of global securities disputes. This is particularly so after the United States largely relinquished this role in Morrison v. National Australia Bank. Whether Imax proves to be a meaningful precedent or simply an aberration will largely depend on whether the court dealt appropriately with the conflict of laws issues at the heart of the case. No author has yet addressed the conflict of laws complications posed by the certification of global class actions in Canada; this Article seeks to fill that void. In particular, I use the Imax case as a lens through which to canvass the conflict of laws issues raised by the certification of global classes. I look at the difficult questions of jurisdiction simpliciter, recognition of judgments, choice of law, parallel proceedings, and notice/procedural rights that need to be addressed now that global classes have come to Canada.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".