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Record W4386304507 · doi:10.18254/s207054760027323-2

Specifics of Canada's Approach to Online Safety

2023· article· en· W4386304507 on OpenAlexaboutno aff
Tatiana Shchukina

Bibliographic record

VenueRussia and America in the 21st Century · 2023
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsObligationLegislationStatutory lawDemocracyGovernment (linguistics)Public relationsInternet privacyBusinessPolitical sciencePublic administrationLawComputer sciencePolitics

Abstract

fetched live from OpenAlex

Online platforms are increasingly central to participation in economic, democratic, cultural and public life. However, such platforms can also be used to threaten and intimidate Canadians and to promote views that target communities, put people's safety at risk, and undermine Canada's social cohesion or democracy. The Government of Canada is committed to putting in place a transparent and accountable regulatory framework for online safety in Canada and confronting online harms while respecting freedom of expression, privacy protection, and the open exchange of ideas and debate online. Now, more than ever, online services must be held responsible for addressing harmful content on their platforms and creating a safe online space that protects all Canadians. The new legislation would set out a statutory requirement for regulated entities to take all reasonable measures to make harmful content inaccessible in Canada. This obligation would require regulated entities to do whatever is reasonable and within their power to monitor for the regulated categories of harmful content on their services, including through the use of automated systems based on algorithms.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.260
Teacher spread0.224 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueRussia and America in the 21st CenturySame topicHate Speech and Cyberbullying DetectionFrench-language works237,207