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Record W1808971161 · doi:10.17645/mac.v3i3.263

Beyond Privacy: Articulating the Broader Harms of Pervasive Mass Surveillance

2015· article· en· W1808971161 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedia and Communication · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Toronto
FundersCanadian Internet Registration Authority
KeywordsInternet privacyHarmValue (mathematics)IntersubjectivitySociologyArgument (complex analysis)The Right to PrivacyRight to privacyNormativeLaw and economicsCyberspacePublic relationsEpistemologyPolitical scienceLawHuman rightsComputer science

Abstract

fetched live from OpenAlex

This article begins by recounting a series of mass surveillance practices conducted by members of the “Five Eyes” spying alliance. While boundary- and intersubjectivity-based theories of privacy register some of the harms linked to such practices I demonstrate how neither are holistically capable of registering these harms. Given these theories’ deficiencies I argue that critiques of signals intelligence surveillance practices can be better grounded on why the practices intrude on basic communicative rights, including those related to privacy. The crux of the argument is that pervasive mass surveillance erodes essential boundaries between public and private spheres by compromising populations’ abilities to freely communicate with one another and, in the process, erodes the integrity of democratic processes and institutions. Such erosions are captured as privacy violations but, ultimately, are more destructive to the fabric of society than are registered by theories of privacy alone. After demonstrating the value of adopting a communicative rights approach to critique signals intelligence surveillance I conclude by arguing that this approach also lets us clarify the international normative implications of such surveillance, that it provides a novel way of conceptualizing legal harm linked to the surveillance, and that it showcases the overall value of focusing on the implications of interfering with communications first, and as such interferences constituting privacy violations second. Ultimately, by adopting this Habermasian inspired mode of analysis we can develop more holistic ways of conceptualizing harms associated with signals intelligence practices than are provided by either boundary- or intersubjective-based theories 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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.306
Teacher spread0.254 · 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