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Record W7128606112 · doi:10.26180/4955855.v1

Network neutrality in the Great White North (and its impact on Canadian culture)

2017· article· W7128606112 on OpenAlexaboutno aff
Jeremy de Beer

Bibliographic record

VenueMonash University · 2017
Typearticle
Language
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsNeutralityNet neutralityLegislatureWhite paperCompetition (biology)Frame (networking)White (mutation)

Abstract

fetched live from OpenAlex

This article contributes to the growing body of network neutrality literature by describing and commenting on recent developments in Canada. There have been and still are ongoing industry practices, regulatory policy proceedings, judicial decisions and pending litigation, and legislative proposals relevant to the issue of network neutrality in Canada. While most of the network neutrality literature has an economic focus, this paper dwells more on implications for Canadian culture. Though the dominant technological and economic discourses about issues like innovation and competition cannot be ignored, these are not the only paradigms with which to frame regulatory and other decision-making. Ultimately, this paper recommends a light-handed cultural policy response one that clearly imposes neutrality obligations but does so in a principled rather than prescriptive manner. Copyright 2009 Jeremy de Beer. No part of this article may be reproduced by any means without the written consent of the publisher.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0250.015
Scholarly communication0.0110.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.016
GPT teacher head0.222
Teacher spread0.207 · 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
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
Published2017
Admission routes1
Has abstractyes

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