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Record W2114470392 · doi:10.1145/1316624.1316632

Privacy in the open

2007· article· en· W2114470392 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

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of British ColumbiaUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceInternet privacyComputer securityInformation privacy

Abstract

fetched live from OpenAlex

The tension between privacy and awareness has been a persistent difficulty in distributed environments that support opportunistic and informal interaction. For example, many awareness systems that display 'always-on' video links or PC screen contents have been perceived as too invasive, even though functional real-world analogues, like open-plan offices, may provide even less privacy than their online counterparts. In this paper we explore the notion of privacy in open-plan real-world environments, in order to learn more about how it might be supported in distributed systems. From interviews and observations in four open-plan offices, we found that attention plays an important role in the management of both confidentiality and solitude. The public nature of paying attention allows people to build understandings of what objects in a space are legitimate targets for attention and allows people to advertise their interest in interaction. Our results add to what is known about how privacy works in real-world spaces, and suggest valuable design ideas that can help improve support for natural privacy control and interaction in distributed awareness systems.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.575
GPT teacher head0.552
Teacher spread0.023 · 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