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Record W4385469174 · doi:10.3138/cjc.2023-0006

Introduction: Policy Portal 4: Regulation

2023· article· en· W4385469174 on OpenAlexaffvenueabout
Leslie Regan Shade

Bibliographic record

VenueCanadian Journal of Communication · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

This is the second issue in a two-part series of the Policy Portal on the theme of regulation.In the first issue, 48.1, four articles addressed an array of regulatory issues including current initiatives in Canadian broadcasting and telecommunication policy, privacy and facial recognition technologies, and children's digital games.This second issue consists of five articles that discuss regulation in Canadian telecommunications, including broadband and wireless spectrum, competition policy in mergers and acquisitions, antitrust options for digital platforms, and the politics and governance of pornography platforms.Woven throughout these articles are considerations of structures of participation in policymaking and modes of public engagement-how best to effectuate meaningful participation and the maintenance and nurturance of the public interest in telecommunications and digital platforms.Through analysis of numerous Canadian Radio-television and Telecommunications Commission (CRTC) proceedings and decisions on the deployment of rural and remote broadband, including, notably, submissions from Indigenous organizations, Heather E. Hudson, Rob McMahon, and Bill Murdoch, in "Beyond Funding: Barriers to Extending Broadband in the Indigenous North," advance policy proposals toward ameliorating barriers.Barriers include wholesale access to transport services and existing support structures, such as poles and towers, access to rights-of-way that are jurisdictionally complex and may include Indigenous reserves and territories, appropriate

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)0.001
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: none
Teacher disagreement score0.776
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.329
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; 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

Citations0
Published2023
Admission routes3
Has abstractyes

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