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Record W7039198997

Law Commission of Ontario to host one-day conference on May 3 examining “Defamation Law in the Internet Age: Where Do We Go From Here?” Registration is now open

2018· article· en· W7039198997 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFreedom of Expression and Defamation
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetCommissionLegal aspects of computingFreedom of expressionIntermediaryField (mathematics)Common lawRule of law
DOInot available

Abstract

fetched live from OpenAlex

It has been said that almost every concept and rule in the field of defamation law has to be reconsidered in light of the Internet. The Law Commission of Ontario’s “Defamation Law and the Internet: Where Do We Go From Here?” conference, held on May 3, 2018, considered whether or how defamation law should be reformed in light of fast-moving and far-reaching developments in law, technology and social values.\nTopics discussed included defamation, online speech and reputation, the relationship between freedom of expression and privacy, whether or how internet intermediaries (such as Facebook or Google) should be responsible for online defamation, internet “content moderation”, dispute resolution, and access to justice.\nThe conference was incredibly successful, with over 140 participants in person and on the live webcast, contributing to the discussion.\nWe invite you to continue the dialogue on the issues raised at the conference.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.084
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0160.005
Scholarly communication0.0080.003
Open science0.0020.003
Research integrity0.0110.006
Insufficient payload (model declined to judge)0.0400.005

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 head0.121
GPT teacher head0.352
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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