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Record W2000959909 · doi:10.1145/1067860.1067868

The language of privacy

2005· article· en· W2000959909 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

VenueACM Transactions on Computer-Human Interaction · 2005
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVocabularyComputer scienceInternet privacySpace (punctuation)Privacy by DesignComputer-supported cooperative workPrivacy softwareWork (physics)Information privacyLinguistics

Abstract

fetched live from OpenAlex

Video media spaces are an excellent crucible for the study of privacy. Their design affords opportunities for misuse, prompts ethical questions, and engenders grave concerns from both users and nonusers. Despite considerable discussion of the privacy problems uncovered in prior work, questions remain as to how to design a privacy-preserving video media space and how to evaluate its effect on privacy. The problem is more deeply rooted than this, however. Privacy is an enormous concept from which a large vocabulary of terms emerges. Disambiguating the meanings of and relationships between these terms facilitates understanding of the link between privacy and design. In this article, we draw from resources in environmental psychology and computer-supported cooperative work (CSCW) to build a broadly and deeply rooted vocabulary for privacy. We relate the vocabulary back to the real and hard problem of designing privacy-preserving video media spaces. In doing so, we facilitate analysis of the privacy-design relationship.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.313
Teacher spread0.291 · 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