Jurisdictional fairness and freezing measures : an analysis in Canadian private international law
Bibliographic record
Abstract
Over the last decade the law of judicial jurisdiction has undergone profound changes in Canada. Chief among the factors that prompted evolution is the recognition of fairness as a guiding principle in the elaboration of jurisdictional rules. This thesis presents the consequences such recognition has already had and should have on the granting of freezing measures, those specific provisional measures aimed at freezing a defendant's assets pending litigation. Having reviewed the freezing measures that can be obtained from Canadian courts, this thesis shows how concerns of fairness to the parties have questioned traditional grounds of jurisdiction and how it has affected the very availability of freezing measures. However, out of concerns of fairness to the plaintiff, Canadian courts with no jurisdiction to hear the merits of a dispute should be ready to assert jurisdiction for the limited purpose of freezing assets. Finally, turning to the scope of those measures, this thesis deals with the issue of extraterritoriality. It is argued that out of concerns of fairness to innocent third parties courts should be extremely cautious in granting extraterritorial measures.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".