Bibliographic record
Abstract
Canada is a bilingual and bi-jurisdictional country. Most provinces and territories are mainly English speaking and have common law as the basis for their legal system. The exception is the province of Quebec which is governed by civil law and where the majority speaks French. However, it must be noted that Quebec civil law has been substantially affected by common law, in particular with respect to discovery rules. The latter are closer to common law discovery rules than they are from, for instance, French civil law. Another important factor for the review of the management of digital evidence in Canada is the existence of different jurisdictions within each province. Canada has a federal system, with a national court system and court systems in each province and territory. The federal courts have limited authority and focus mainly on immigration, intellectual property, maritime and other 'national concerns'. The courts of appeal, the superior (or supreme, depending on the province or territory) courts, the provincial courts and administrative tribunals in each province hear the bulk of the cases. Provincial laws dictate the practice of civil litigation, and discovery is governed by rules of civil procedure or, in Quebec, the Code of Civil Procedure (C.C.P.).
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 | 0.013 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
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".