MétaCan
Menu
Back to cohort
Record W2077096460 · doi:10.4018/ijcbpl.2014040101

Disclosure and Privacy Settings on Social Networking Sites

2014· article· en· W2077096460 on OpenAlexaff
Karin Archer, Eileen Wood, Amanda Nosko, Domenica De Pasquale, Seija Molema, Emily Christofides

Bibliographic record

VenueInternational Journal of Cyber Behavior Psychology and Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsInternet privacyIntervention (counseling)Construct (python library)Computer scienceOnline videoInformation privacyPrivacy protectionPsychologyMultimedia

Abstract

fetched live from OpenAlex

The present study evaluated a video-based intervention designed to permit users of social networking to make informed decisions about the information they disclosed online. The videos provided information regarding potential risks of disclosure and well as step-by-step instructions on privacy setting use. Novice (n=40) and experienced (n=40). FacebookTM users were randomly assigned to either the video intervention condition, or given the choice to watch the video intervention then were asked to construct a new FacebookTM account or work on their existing account. Viewing the video encouraged greater use of privacy settings as well as use of more restrictive privacy settings. Gender differences revealed greater use of privacy settings among women. Experienced users continued to disclose more than novice users, however, they increased their use of privacy settings which restricted the availability of the disclosed information. This study shows promising use of direct and explicit instruction in the teaching of privacy online.

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.

How this classification was reachedexpand

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 score0.270
Threshold uncertainty score0.346

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 communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.390
Teacher spread0.359 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Cyber Behavior Psychology and LearningSame topicImpact of Technology on AdolescentsFrench-language works237,207