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Record W6986409284

The Personal Information Protection\nand Electronic Documents Act: A Lost\nOpportunity to Democratize Canada's\n"Technological Society"

2000· article· en· W6986409284 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationPersonally identifiable informationPrivacy policyPromulgationParliamentCharterInformation privacyPrivacy lawGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Bill C-6, more recently known as the Personal Information Protection and Electronic Documents Act, is promoted by the Canadian government as privacy legislation to protect Canadians' personal information. This paper explores that characterization and concludes that it is inaccurate and misleading. The problems that motivated a response by Parliament are the proliferation and commercial importance of personal information, concerns Canadians have about its uncontrolled use by the private sector and the inadequacy of existing law to address those concerns. However, the Act has not responded to these problems. There are several reasons for this, primarily the disproportionate and antidemocratic importance of business interests in the promulgation of the legislation and the characterization of privacy in market terms rather than in the language of human rights and long-term policy objectives. The Act's failure to achieve its substantive goals is demonstrated by comparing it with other models of privacy protection, such as the Privacy Charter proposed by the House of Commons Standing Committee on Human Rights, equivalent legislation in Quebec and the Australian Privacy Charter. Ultimately, the paper proposes solutions that would be more responsive to citizens' privacy concerns.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.253
Teacher spread0.239 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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