MétaCan
Menu
Back to cohort
Record W2793082563 · doi:10.14236/ewic/hci2017.45

Engaging Children About Online Privacy Through Storytelling in an Interactive Comic

2017· article· en· W2793082563 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

VenueElectronic workshops in computing · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComicsReading (process)StorytellingInternet privacyPsychologyControl (management)Computer scienceMultimediaNarrativeArtificial intelligenceArtPolitical scienceLiterature

Abstract

fetched live from OpenAlex

Children’s privacy is put at risk through online sharing of location-based information. We study the effectiveness of an educational interactive comic on improving 11- to 13-year-old children’s privacy knowledge and behaviour immediately and one week after reading. Children’s privacy knowledge increased after reading either the comic or the text-only control, but the comic promoted superior knowledge retention a week later and was more successful at influencing children’s reported privacy behaviour than the control. Our 22 child-parent pairs found the comic facilitated learning for children, engaging, and easy to use. We discuss the implication on children’s short and long-term knowledge retention and behaviour, and the educational potential of comics at addressing the challenges of privacy and security education for children.

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)0.001
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 score0.349
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.027
GPT teacher head0.348
Teacher spread0.322 · 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