Theorizing Privacy in a Liberal Democracy: Canadian Jurisprudence, Anti-Terrorism, and Social Memory After 9/11
Bibliographic record
Abstract
Abstract The creation of new search powers in the Canadian Anti-Terrorism Act post-9/11 to make citizens more transparent to state surveillance was less a new phenomenon than an extension of preexisting tendencies to make citizens transparent to the state, so the risks they pose can be efficiently managed. However, 9/11 brought about a shift in the ways in which the Supreme Court of Canada talked about terrorism; terrorism was no longer placed on a continuum of criminal activity but was elevated to a threat to Canadian values as a whole. I argue that, paradoxically, this shift reconnected the Court to earlier discourses about privacy as an essential element of democratic governance and reinvigorated narratives around the importance of the public-private boundary to democratic relationships by situating privacy within narratives informed by social memory. From this perspective, privacy can be conceptualized as a status claim: as citizens, we are entitled to privacy because privacy is the boundary that creates right relationships between citizens and between citizens and the state. This avoids pitting privacy as an individual right against societal interests in transparency because it more fully actualizes Priscilla Regan’s call to theorize the value of privacy as a public good central to liberal democratic governance. This conception also reconnects Alan Westin’s original understanding of privacy as an element of liberal democracy to the sociological research he drew on, enriching the liberal conception of privacy by locating it in the intersubjective communication of cultural actors living in community .
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.043 | 0.128 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".