The Right Answers to Ontario's Jurisdictional Questions: Dismiss, Stay or Set Service Aside
Bibliographic record
Abstract
The increasing amount of international litigation in Ontario has been accompanied by a parallel increase in the number of ways defendants can challenge the appropriateness of Ontario proceedings. The Rules of Civil Procedure are reasonably clear in respect of some of these challenges, but not others. In particular, challenges based on the lack of a real and substantial connection to Ontario do not fit easily into these provisions. In such a challenge, the defendant is not seeking to stay the proceeding, in the sense of suspending it pending some development. Rather, the defendant is seeking to have the litigation dismissed due to Ontario’s lack of jurisdiction. This important distinction is often blurred by the courts in their analysis. The different challenges each give rise to specific remedial orders. In particular, the court can set aside the service of process, dismiss the proceedings or stay the proceedings. In several cases the courts have granted the wrong order, most notably where they have stayed proceedings for lack of jurisdiction. If the court lacks jurisdiction, there is no proceeding to be stayed and the appropriate order is a dismissal.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".