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Record W2911278515 · doi:10.1515/til-2019-0003

Re-reading Westin

2019· article· en· W2911278515 on OpenAlexaff
Lisa M. Austin

Bibliographic record

VenueTheoretical Inquiries in Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInternet privacyPersonally identifiable informationAnonymityInformation privacyReading (process)Privacy policyFocus (optics)Privacy by DesignControl (management)NegotiationRight to privacyCuriosityComputer sciencePsychologySocial psychologyComputer securityLawPolitical science

Abstract

fetched live from OpenAlex

Abstract Alan Westin’s work Privacy and Freedom remains foundational to the field of privacy, and Westin is frequently cited for his definition of privacy as control over personal information. However, Westin’s full definition of privacy is much more complex than this statement, describing four states of privacy (solitude, intimacy, anonymity, and reserve) that one achieves through physical or psychological means. The “claim” of privacy involves negotiating a balance between a desire for disclosure and social participation and a desire for withdrawal into one of the “states” of privacy. Influencing this adjustment process are social norms (and surveillance to enforce social norms), environmental conditions, and the curiosity of others. In this Article, I draw upon this complexity in order to reread Westin’s definition of privacy as a claim of control over personal information and use this rereading to understand how the law should protect and promote privacy in the twenty-first century. I argue that the law should focus on securing meaningful privacy choices rather than on individual control over personal information. Meaningful choice requires that our informational infrastructure, and the social practices that it enables, make states of privacy available for choice along with the means of attaining them. To enable such meaningful individual choice, we need to shift our attention from a focus on individuals to a more systemic focus on our public norms and built infrastructure. Otherwise we risk protecting a narrow understanding of individual control, while ignoring a more general and systematic erosion of privacy .

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0070.032
Scholarly communication0.0110.021
Open science0.0020.004
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0060.002

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 head0.026
GPT teacher head0.330
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations25
Published2019
Admission routes1
Has abstractyes

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