Translating the Public Imaginary: The Narrative Aesthetics of Public Engagement in Canadian Broadcasting Policy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.036 | 0.077 |
| Scholarly communication | 0.029 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".