Canadian Hack-Back?: A Consideration of the Canadian Legal Framework for Private-Sector Active Cyber Defence
Bibliographic record
Abstract
In recent years, a debate has emerged over the extent to which victims of cyber security intrusions should be permitted to conduct activities in response — in particular, activities with effects in networks outside the victim’s own. Such controversial efforts are often referred to as active cyber defence (ACD) or, more colloquially, as “hack-back.” While multiple researchers have written about how private-actor ACD fits within the United States legal framework, this topic remains understudied from a Canadian perspective, raising the question of how Canadian legislation may address ACD. Currently, Canadian legislation implicitly prohibits most, if not all, ACD efforts, but international law likely leaves room for countries to legalize certain forms of ACD. Going forward, there may be a significant benefit to Canadian legalization of ACD if these efforts are limited to “intelligence gathering” and constrained by strict government oversight.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".