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Record W4242851009 · doi:10.32920/ryerson.14649420.v1

Managing the mosaic: diversity of voices and deliberative policy making in English Canadian media

2021· preprint· en· W4242851009 on OpenAlexaffabout
Sylvia Blake

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsViewpointsDiversity (politics)Deliberative democracyPolitical sciencePublic relationsPublicityCommissionDeliberationStakeholderHeadlineSociologyPublic administrationDemocracyLawPoliticsAdvertisingBusiness

Abstract

fetched live from OpenAlex

This study investigates viewpoints on policy for diversity in media subsequent to the Canadian Radio-television and Telecommunications Commission (CRTC)’s 2007-5 diversity of voices proceedings and subsequent CRTC 2008-4 regulatory changes. The policy proceedings were designed to aggregate and act upon the many policy preferences and conceptions of media diversity within Canada’s complex media mosaic. Research reported here uses Q methodology, complemented with conventional survey questions and open-ended qualitative questions, to identify and interpret the plurality of subjective viewpoints surrounding the diversity debate and the CRTC’s deliberative policymaking processes. Research identified four principal viewpoints regarding policy for media diversity, based on concerns about minority representation, industry consolidation, Canadian cultural expression, and a comprehensive marketplace of ideas. It also considers various stakeholder viewpoints on the CRTC’s 2007-5 deliberative proceedings, and the extent to which the Commission’s deliberative processes meet the four deliberative democratic pillars of inclusiveness, equality, reasonableness and publicity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.038
GPT teacher head0.311
Teacher spread0.272 · 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 designQualitative
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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