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Record W1868922036 · doi:10.1002/meet.2014.14505101013

Understanding networked youth and online privacy: Questions, methods and implications

2014· article· en· W1868922036 on OpenAlexaff
Devon Greyson, Denise E. Agosto, Eric M. Meyers, Mega Subramaniam, June Abbas

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of British Columbia
FundersUniversity of Oklahoma
KeywordsInternet privacyVariety (cybernetics)Social mediaHarmBrainstormingNegotiationOnline participationPsychologyDigital mediaSession (web analytics)Public relationsSociologyComputer scienceThe InternetSocial psychologyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

ABSTRACT Young people's social practices online and use of digital media have been conceptualized both as an opportunity and a threat to society. While the mass media has often focused on moral panic related to dramatic examples of social media‐related harm to young people, research within Library and Information Science has taken multiple methodological approaches to investigate the ways digital youth navigate and negotiate privacy in online spaces. Findings from such studies offer insight into youth cultures and technology design that supports online privacy. This panel will demonstrate a variety of projects and approaches that have been taken to investigate privacy issues related to young people's online interactions and practices, and the social and design implications of emerging findings for our understanding of young people and online privacy. We will also lead audience members in an interactive brainstorming session centered around various research scenarios, asking participants to consider the strengths and weaknesses of specific research questions and methods in addressing adults’ concerns related to youth and online privacy. We will conclude with an interactive discussion of recommended future research directions for enhancing our understanding of young people's privacy practices.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
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 head0.061
GPT teacher head0.361
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations2
Published2014
Admission routes1
Has abstractyes

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