Reforming the bifurcated process under section 38 of the Canada Evidence Act
Bibliographic record
Abstract
Objections to the disclosure of information under section 38 of the Canada Evidence Act must be adjudicated in the first instance by the Federal Court even when they arise in proceedings before other courts or tribunals. This bifurcated process has come under serious criticism as inefficient and ineffective, especially in relation to criminal proceedings. This article examines the principal concerns that have been raised about the bifurcated process and argues that radical reforms to the current process are not warranted. Rather, with effective coordination between the Federal Court and the underlying proceeding, a bifurcated process in which the Federal Court adjudicates most if not all privilege claims under section 38 of the Canada Evidence Act is the optimal approach and should be preserved.
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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 | 0.038 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".