Algorithms, Expression and the Charter: A Way Forward For Canadian Courts
Bibliographic record
Abstract
As a result of rapid advances in technology and computer programming, algorithms are increasingly able to generate expressive material. In light of these advances, it is inevitable that courts will be asked to determine whether this algorithmically generated content is protected expression under section 2(b) of the Canadian Charter of Rights and Freedoms. Although algorithmically generated content can serve many of the same constitutionally-protected purposes as human expression, this paper explains why the Supreme Court of Canada’s current framework is inadequate for use in the context of algorithmically generated content. This paper offers a proactive and principled solution that is consistent with the fundamental principles of freedom of expression articulated by the Court in R v Keegstra. This solution allows content that upholds the Keegstra principles to be protected despite the fact that the algorithm’s creator may not have contemplated the specific content.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".