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Record W2026916269 · doi:10.1080/13614560701709861

Designing for privacy in personal learning spaces

2007· article· en· W2026916269 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

VenueNew Review of Hypermedia and Multimedia · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer sciencePersonally identifiable informationGrounded theoryInternet privacySpace (punctuation)Information sharingGroup information managementInformation privacyPrivacy by DesignPersonal information managementKnowledge managementData scienceInformation systemWorld Wide WebManagement information systemsComputer securityQualitative researchSociology

Abstract

fetched live from OpenAlex

We present the results of a study of information sharing behaviour of the users of a personal learning space. Our study uses grounded theory methodology and involves 12 K12 students who have used a personal learning space for over a year. The resulting grounded theory suggests that users’ preferences regarding privacy of their artefacts in such an environment depends on a number of factors, including the current stage in the artefact's life cycle, the nature of trust between the owner and the receiver of information, and the dynamics of the group or community within which the information is being shared. Based on our findings, we propose a framework for understanding and designing privacy control mechanisms for personal learning spaces that reflect users’ mental model of information privacy. To illustrate these principles in practice, we describe the privacy management mechanisms of OpnTag, an application we have designed as a test bed for social information management.

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)0.004
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
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 head0.195
GPT teacher head0.442
Teacher spread0.247 · 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