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Record W4231149558 · doi:10.32920/ryerson.14664288

Public Sphere Disruption : Public Service Broadcasting and the CBC at the Digital Crossroads

2021· preprint· en· W4231149558 on OpenAlexaffabout
Scott William Baird

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsToronto Metropolitan UniversityYork UniversityCentre for Social InnovationUniversity of Ottawa
Fundersnot available
KeywordsPublic spherePolitical scienceElement (criminal law)Public broadcastingCorporationPublic administrationContext (archaeology)Broadcasting (networking)Public policyPublic servicePublic relationsLawGeographyPoliticsComputer scienceComputer security

Abstract

fetched live from OpenAlex

Public broadcasting is traditionally thought to be an essential element to public spheres. This paper charts how this relationship is formed, and then demonstrates how it is threatened in the Canadian context. Canada’s public broadcaster, the Canadian Broadcasting Corporation, has digital policies like Strategy 2020: A Space for Us All which suggests CBC is pivoting away from its relationship with the public sphere, and in some ways weakening the Canadian public sphere. Accordingly, this paper looks at the claims charged about this policy, particularly from Taylor (2016), and considers how it and similar digital policies affect the CBC as an element of the Canadian public sphere. While the paper finds CBC digital policies benefit the public sphere, the majority put into action hinder CBC’s relationship to the Canadian public sphere. Overall, this MRP highlights the importance of considering the philosophy of the public sphere when developing public media policy.

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)0.013
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 score0.124
Threshold uncertainty score0.760

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0240.047
Scholarly communication0.0280.013
Open science0.0020.011
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0130.001

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 head0.098
GPT teacher head0.317
Teacher spread0.220 · 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
Published2021
Admission routes2
Has abstractyes

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