A new innovative method to evaluate public news broadcasting: Preserving democracy, culture, and identity during the first AI revolution
Bibliographic record
Abstract
As the first AI revolution rapidly eliminates numerous journalism, reporting, and news writing jobs, the debate over taxpayer-funded public broadcasting entities in some countries gains momentum. The potential threats posed by AI-generated content, unregulated or self-regulated social media, and radical social networking sites to public opinion and election results are concerning. This study presents the first cost-benefit analysis of publicly funded broadcasting, with a focus on the CBC/Radio-Canada. The benefits are estimated using mathematical models via the mass (Canadian Newsstream database) and social media (YouTube). CBC/Radio-Canada has contributed 471,706 newsprints via the news wire, while also generating 126,436 videos across 16 YouTube accounts, with 4,031,467,452 views and 8,065,340 subscribers, resulting in a benefit-to-cost ratio of 2.17 × 10 5 :1. Therefore, CBC/Radio-Canada, as a taxpayer-funded entity, is highly cost-effective and efficient. CBC/Radio-Canada further contributes billions of dollars annually to the local and national economies, while also playing a vital role in preserving the cultures and identities of its many nations, promoting official languages, multiculturalism, tolerance, national cohesion, and international influence, and, most importantly, democracy in an ever-changing world. It is recommended that CBC/Radio-Canada begin offering more Canadian news and content in local, rural, French, Indigenous, Inuit, and other languages.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".