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Record W2802075775 · doi:10.5206/elip.v1i1.366

Be Internet Awesome

2018· article· en· W2802075775 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueEmerging Library & Information Perspectives · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsWestern University
Fundersnot available
KeywordsThe InternetInternet privacyElement (criminal law)Internet researchWorld Wide WebComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Child safety concerns are a crucial element of conversations about Internet usage, with numerous Internet safety programs seeking to help children protect themselves while online. Google joins these existing efforts with its release of the Be Internet Awesome program, which is designed to teach children how to safely and responsibly explore the Internet. Existing reviews of the program do not include critical examinations of its content and its recommendations for children. As such, this paper critically examines the content and underlying messages of Be Internet Awesome to discover how it conceptualizes and presents Internet safety threats. This analysis reveals that although Be Internet Awesome is well designed and addresses common Internet safety themes, the awareness it aims to help children gain is not comprehensive. Specifically, the program fails to consider the usage of information past a surface level, ignores elements outside of the user’s control, and portrays Google as a benevolent and authoritative Internet expert.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.014
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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