Citizen Involvement during the CRTC’s Let’s Talk TV Consultation
Bibliographic record
Abstract
The authors analyze citizen engagement efforts undertaken by the Canadian Radio-television and Telecommunications Commission (CRTC) between 2013 and 2015, under the aegis of the Let’s Talk TV (LTTV) initiative. They explore whether and how input provided by citizen-consumers during LTTV demonstrates activism through engagements in policy discussions. The article first delineates the timing and scope of activities conducted by the Canadian regulator to involve individual Canadians in reshaping digital television services. The CRTC’s early decisions are then compared to the authors’ analyses of input from hundreds of people invited to answer the CRTC’s general questions online. Comparing the decisions made to date with citizen-consumer responses, the article points to contradictions and disconnects between the authors’ findings and the CRTC’s initial decisions.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".