Surveillance Capitalism and the Charter: Infusing the Common Law with Charter Values
Bibliographic record
Abstract
The premise of this paper is not new. Over two decades ago, John D R Craig argued the Charter value of privacy “should now be given effect in the Canadian common law, through a principled development of the tort category that affords relief for intentional interferences with the person.” This paper, however, builds on newer case law, including Jones, to explore and reinvigorate the promise of horizontality — that is, the application of constitutional values to private law. In so doing, I argue that Charter values of privacy must naturally fuse with common law to constrain private corporations’ data practices, facilitated through a combination of horizontality and existing legal principles. I proceed by exploring how privacy has been interpreted in the context of section 8 of the Charter, as well as the impact of surveillance capitalism on prevailing understandings of privacy. I then turn to what I view as the horizontal promise of section 8, its potential to bind private actors through its influence on private law, and the implications of such developments. I also briefly discuss the quasi-public function that some corporate surveillers have adopted.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".