Twisted into knots: Canada’s challenges in lawful access to encrypted communications
Bibliographic record
Abstract
This article addresses the Canadian law governing ‘lawful access’ to potentially encrypted data-in-motion; that is, communications done through electronic means. This article begins by outlining the core agencies responsible for counterterrorism investigations in Canada, and the recent public debate and government consultation on encryption. Next, we identify how older laws designed for a different era may be leveraged to force service and platform providers to assist law enforcement and the Canadian Security Intelligence Service by decrypting communications and data. We will also touch on the legal capacity of these organizations to develop their own ‘workarounds’, including the role of Canada’s signals intelligence agency, the Communications Security Establishment. Throughout, we highlight how Canada’s long-standing ‘intelligence to evidence’ problem affects and, arguably exacerbates, the encryption-prompted ‘going dark’ phenomenon and consequently impairs Canadian counterterrorism efforts. We predict legal reform resolving the ‘going dark’ issue will be impossible without modernization of Canada’s disclosure regime.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".