Simulating public interest: the issue of the public voice in the fee-for-carriage debate
Bibliographic record
Abstract
"One of the most fractious Canadian Radio-television and Telecommunications Commission (CRTC, or the Commission) policy hearings on record has recently come to a close. This was no run-of-the-mill, watch-the-paint-dry policy hearing. Tempers and passions flared as two industry titans, over-the-air (OTA) broadcasters, such as CTV and Canwest Global, and broadcast distribution undertakings (BDUs) such as Shaw Communications, Bell Canada and Rogers Inc. fought the battle of their lives over an issue called fee-for-carriage (FFC). The media covered the issues day in and day out. Canadians bombarded the CRTC with dose to 200,000 comments and the Government of Canada forced the CRTC to hold an additional hearing just to address the impact the decision could have on the public. With extensive media coverage and uncharacteristically active public participation, could this public policy process be deemed 'democracy in action'? This paper will argue that this is not the case. Through a discourse analysis of the debate within two distinctly differentiated public spheres -- 1) the battling media campaigns and 2) the CRTC public hearings in November and December of 2009 -- this paper will show that the public's ability to define its own interest, using its own voice, is tarnished to such a severe degree that this policy process fails"--From Introduction (page 3).
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.064 |
| Scholarly communication | 0.036 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".