The Space Between Rule 20 and 21: The Evidentiary Burden on Certification
Bibliographic record
Abstract
This paper attempts to develop the meaning of the “some basis in fact” evidentiary requirement on certification. Since the Supreme Court of Canada articulated this concept as the appropriate evidentiary test in Hollick v. Toronto (City), courts have struggled to provide a specific factual threshold to satisfy this inquiry. The paper canvasses the jurisprudence to this effect and attempts to provide some answers to this elusive evidentiary component, particularly as it relates to each statutory criteria of the certification test, and which party bears this evidentiary burden in what circumstances. The courts’ treatment of the precarious balance for plaintiffs’ counsel in striking a sufficient evidentiary basis, without tendering superfluous merits based evidence is also examined, as is the treatment of evidence to either support or refute the propriety of an aggregate assessment of damages. Ultimately, the article concludes that satisfying the malleable concept of “some basis in fact” often lies someplace between Rule 20 and Rule 21: while there is no genuine issue for trial test on certification, something more than the plain and obvious test is clearly required. Where this line is drawn on certification continues to evolve, dependent always on the specific facts of each certification motion.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".