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Record W2486072240 · doi:10.1145/2930674.2930716

From Nosy Little Brothers to Stranger-Danger

2016· article· en· W2486072240 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
TopicChild Development and Digital Technology
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHarmInternet privacyAdversaryChild protectionPsychologyChild safetyVariety (cybernetics)Computer securitySocial psychologyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

The rise in mobile media use by children has heightened parents' concerns for their online safety. Through semi-structured interviews of parent-child dyads, we explore the perceived privacy and security threats faced by children aged seven to eleven along with the protection mechanisms employed. We identified four models of privacy held by children. Furthermore, we found that children's concerns fit into four child-adversary threat models: child-peers, child-media, child-strangers, and child-parents. Their concerns differed from the five threat models held by the parents: child-peers, child-media, child-strangers, child-technology, and child-self. Parents used a variety of protection strategies to minimize children's exposure to external threats. In reality, however, our results suggest that security and privacy risks from an internal family member or a friend are far more common than harm from outsiders.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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

Citations73
Published2016
Admission routes2
Has abstractyes

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