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Record W2918131609 · doi:10.22230/cjc.2019v44n1a3381

Translating the Public Imaginary: The Narrative Aesthetics of Public Engagement in Canadian Broadcasting Policy

2019· article· en· W2918131609 on OpenAlexaffvenueabout
Michael Lithgow

Bibliographic record

VenueCanadian Journal of Communication · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsLegitimacyNarrativeCommissionPublic discourseSociologyPolitical scienceDiscourse analysisThe ImaginaryPublic policyBroadcasting (networking)Media studiesAestheticsPublic relationsPsychologyArtLawLinguisticsLiteraturePoliticsPsychoanalysis

Abstract

fetched live from OpenAlex

Background In public proceedings, professionalized discourses often reflect markedly different communicative strategies than those used by members of the general public. Analysis This article describes the findings of an aesthetic discourse analysis of public submissions to one of the largest public processes ever held by the Canadian Radio-television and Telecommunications Commission (CRTC), the Let’s Talk TV review of television regulation in Canada. Conclusions and implications Public submissions demonstrated heartfelt, affective, psychologically complex, and sometimes ambiguous expressions of desire. A routine tactic engaged in public submissions was “narrative aesthetics”—the implicit and explicit use of story structures to shape aspects of discourse legitimacy. The discursive landscape revealed gaps between public sensibilities and those legitimized by policy decisions, suggesting a territory of public experience more complex than the social realities reflected in policy discourse outcomes.

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)0.002
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 score0.828
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.085
GPT teacher head0.279
Teacher spread0.194 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

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