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Record W2268851905 · doi:10.32920/22227775.v1

Privacy Law in the United States, the EU and Canada: The Allure of the Middle Ground

2023· article· en· W2268851905 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

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsToronto Metropolitan University
FundersBrandeis University
KeywordsInformation privacy lawPersonally identifiable informationDignityJurisdictionFTC Fair Information PracticeInformation privacyPrivacy lawEuropean unionData Protection Act 1998AutonomyPrivacy policyGovernment (linguistics)Political scienceData Protection DirectiveBusinessLawPublic administrationInternational tradeEuropean Union law

Abstract

fetched live from OpenAlex

<p>Privacy and personal information are regulated differently in the European Union (EU), the United Sates (US) and Canada. The EU and Canada centrally supervise the private sector's use of personal data, whereas the US regulation of the private sector is minimal. These differences emanate from distinct conceptual bases for privacy in each jurisdiction. In the US, privacy protection is essentially liberty protection, i.e. protection from government. For Europeans, privacy protects dignity or their public image. In Canada, privacy protection is focused on individual autonomy through personal control of information. We propose the Canadian model as a conceptual middle ground between the EU and the US, as a basis for future American privacy protection.</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.276
Teacher spread0.222 · 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

Quick stats

Citations45
Published2023
Admission routes2
Has abstractyes

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