FREEDOM CONVOY FEVER: Social Media and the Reasonable Expectation of Privacy in the Artificially Intelligent Surveillance State
Bibliographic record
Abstract
The 2022 “Freedom Convoy” in Ottawa attracted widespread attention across traditional and social media outlets as the demonstration evolved into a long-drawn-out standoff between protestors and state officials. In the ensuing aftermath, the outpour of images and videos that flooded social media during the protest were subsequently used by law enforcement to arrest and charge individuals involved. This inspired an examination of the constitutional status of these “seizures.” Using the Freedom Convoy as a backdrop, this paper examines the reasonable expectation of privacy in information shared on social media, beginning with a discussion of the influence that artificial intelligence and machine learning have on modern policing. A broad discussion of whether a reasonable expectation of privacy exists in this area of the law then follows, and the difficulty of overcoming the doctrine of abandonment in establishing a privacy interest is noted. The issue of abandonment is then re-evaluated in light of recent jurisprudence from the Supreme Court of Canada, as well as the Mosaic Theory of the Fourth Amendment from United States v. Maynard. A conclusion is then presented, and suggestions are made concerning future research efforts.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".