A Jurisprudential “House of Cards”: The Power to Exclude Improperly Obtained Evidence in Civil Proceedings
Bibliographic record
Abstract
Prior to the enactment of the Charter of Rights and Freedoms, courts were extremely reticent to exclude evidence that had been improperly obtained, preferring instead to advance the “search for truth” by admitting any proof relevant to the matter being tried. In criminal cases, this changed through section 24(2) of the Charter, which provided a specific route to exclusion. But in civil cases, there was no legislated change to the common law. Notwithstanding this fact, trial courts over the past 25 years have begun recognizing a power to exclude evidence that was illegally obtained by a party to the case. Despite the emerging consensus that such a power exists, the relevant jurisprudence reveals a reliance upon questionable lines of reasoning in which the cited authorities are nothing more than other trial level decisions that have done the same, a veritable legal “house of cards”. This development prompts two significant questions that shall be explored in this article: (1) can judges exclude evidence in this manner; and (2) if they can, should they?
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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 | 0.062 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.078 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".