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Record W1977055283 · doi:10.1162/105474605775196571

Sousveillance and Cyborglogs: A 30-Year Empirical Voyage through Ethical, Legal, and Policy Issues

2005· article· en· W1977055283 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.

Bibliographic record

VenuePRESENCE Virtual and Augmented Reality · 2005
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Toronto
FundersDefense Advanced Research Projects Agency
KeywordsVariety (cybernetics)LaggingAmbiguityInternet privacyImplementationNarrativeComputer scienceSociologyPublic relationsPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the author's own personal experiences, experiments, and lifelong narrative of inventing, designing, building, and living with a variety of body-borne computer-based visual information capture and mediation devices. The emphasis is not just on the devices themselves, but on certain social, privacy, ethical, and legal questions and challenges that have arisen from actual experiences with lifelong video capture, processing, transmission, and dissemination in a variety of different everyday cultural settings over the past 30 years. The most interesting of these accidentally-found questions pertain to: (1) inverse surveillance (body-borne audiovisual and other sensor capture, storage, recall, and processing, known in the research literature as “sousveillance”); and (2) the epistemology of freewill and metaphysics of choice that seems to arise from an apparent reversal of the now pervasive and ubiquitous notion of surveillance. Extrapolating from these lessons, several hypotheses are presented, including: (1) sousveillance, like surveillance, will be driven by rapid development of new technology, leaving legal frameworks lagging behind technology; (2) the growth of sousveillance will accelerate greatly when implementations come with other non-sousveillance uses (e.g., camera phones because of their strategic ambiguity with regard to whether they are being used to take a picture or for just a voice call); (3) legal frameworks will tend to support rather than oppose sousveillance; (4) such legal protections will favor video sousveillance over video surveillance just as they now favor audio sousveillance over audio surveillance; (5) such legal protections will emerge first for the disabled (e.g., the visually impaired); and will then expand to encompass other legitimate and beneficial uses of sousveillance (personal safety, evidence gathering, etc.); (6) a person wishing to do lifelong sousveillance is deserving of certain legal protections liabilizing others who might attempt to disrupt continuity of evidence.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.039
GPT teacher head0.364
Teacher spread0.326 · 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