Case Comment: British Columbia (Attorney General) v. Brecknell
Bibliographic record
Abstract
In a day and age where a large portion of both innocent and criminal communications travel across the border and then reside on servers outside of the country, many Canadian police and prosecutors were understandably excited by the British Columbia Court of Appeal’s decision in Brecknell. This case concludes that a Canadian court can order an entity that is only ‘‘virtually present” to produce records pursuant to a Criminal Code production order.\nWhile it is a case that deals with a compelling issue faced by Canadian law enforcement in an environment where hundreds of such orders are issued naming US companies and are generally followed by them, the decision is wrongly decided for a number of reasons. The British Columbia Court of Appeal erred in determining that a legal person who solely has a ‘‘virtual presence” in Canada is, in law, ‘‘present in Canada” for the purposes of the Criminal Code. In addition, the Court of Appeal misapplied the existing authorities on domestic and international law to determine that the Criminal Code has extraterritorial effect. Finally, an ex parte appeal that departed significantly from our adversarial system should be of questionable precedential value.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".