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Record W7133085827

Protecting Privacy Through Quasi-Constitutional Legislation

2022· dissertation· W7133085827 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldSocial Sciences
TopicData Privacy and Cybersecurity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStatuteLegislationInformation privacyPrivacy lawInformation privacy lawData Protection Act 1998Personally identifiable informationFTC Fair Information Practice
DOInot available

Abstract

fetched live from OpenAlex

Recent years have witnessed a growing awareness of the serious erosion of personal privacy in an increasingly digital world in which the accumulation, aggregation, disclosure, and sale of personal information about individuals has become routine. While there is no constitutional right to privacy in Canada, the recognition of privacy statutes as quasi-constitutional potentially offers the prospect of stronger legal protection for this fundamental right. In this thesis, I assess what quasi-constitutionality means by exploring laws between the constitutional and the ordinary in other jurisdictions and then using this framework to map the broader category of quasi-constitutional statutes in Canada. I argue for a more expansive, dialogic conception of the consequences of quasi-constitutionality based on the fundamental rights protected within such legislation. Following this, I develop a model of quasi-constitutional law reform and then apply this to the proposal to replace privacy legislation governing the private sector at the federal level.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0070.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.000

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.

Opus teacher head0.051
GPT teacher head0.405
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it