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Record W2107122082 · doi:10.1145/1135777.1135801

Examining the content and privacy of web browsing incidental information

2006· article· en· W2107122082 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

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceConsistency (knowledge bases)World Wide WebInformation retrievalField (mathematics)Task (project management)Internet privacyPartition (number theory)Scheme (mathematics)Information privacyArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This research examines the privacy comfort levels of participants if others can view traces of their web browsing activity. During a week-long field study, participants used an electronic diary daily to annotate each web page visited with a privacy level. Content categories were used by participants to theoretically specify their privacy comfort for each category and by researchers to partition participants' actual browsing. The content categories were clustered into groups based on the dominant privacy levels applied to the pages. Inconsistencies between participants in their privacy ratings of categories suggest that a general privacy management scheme is inappropriate. Participants' consistency within categories suggests that a personalized scheme may be feasible; however a more fine-grained approach to classification is required to improve results for sites that tend to be general, of multiple task purposes, or dynamic in content.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.989

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.277
Teacher spread0.225 · 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

Quick stats

Citations18
Published2006
Admission routes2
Has abstractyes

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