Criminal Law and Digital Technologies: An Institutional Approach to Rule Creation in a Rapidly Advancing and Complex Setting
Bibliographic record
Abstract
Courts and legislatures in Canada and around the world have struggled to respond effectively and efficiently to the challenges posed by the use of rapidly advancing and complex technologies. As a result, scholars have debated the appropriate role of each institution with respect to governing privacy in the digital age. This debate has provided foundational evidence upon which to develop a normative framework for governing digital privacy. Yet, the Canadian literature has only sparsely addressed the ability of Canadian legislatures to respond to the challenges presented by the use of digital technologies. This article begins to fill the gap in the literature by asking whether Parliament has been able to reply to the use of complex and rapidly advancing technologies in an efficient, coherent, and fair manner. I conclude that Parliament’s legislative framework for governing state intrusions into digital privacy has been patchwork and inconsistent. After comparing these findings to the literature on the relative institutional capacity of courts, I outline a general strategy for ensuring each institution tasked with governing digital privacy is working to its strengths, not its weaknesses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".