Strengthening Canadian Television Content: Creation, Discovery and Export in a Digital World
Bibliographic record
Abstract
In April 2016, the Canadian government announced a consultation on how to best create Canadian content in a digital world. On September 28, 2017, the government released its Creative Canada Policy Framework, a high-level overview of cultural policy direction that summarized initiatives to date and announced some new directions and next steps. Among the next steps is a review of the Broadcasting and Telecommunications Acts, preceded by a Canadian Radio-television and Telecommunications Commission (CRTC) review of new and traditional distribution models and their capacity to support Canadian programming. This examination and modernization of cultural policy is timely. The world of Canadian content regulation was developed in an earlier analog environment. Broadcasting was largely a closed system. This meant that all parts of the broadcasting system could be regulated and most were. In addition, there is a system of subsidies designed to help create more domestic content. But the broadcasting system is no longer closed. High-quality television programming is available from the Internet and Canadians are avid consumers. When TV is delivered over the Internet, none of the Canadian regulations apply. This state of affairs creates two related problems. First, if Internet-delivered TV continues to increase in popularity, this could lead to a significant decline in the amount of available Canadian television content, at least in the regulated system. Second, if Canadian broadcasters and cable companies are regulated, and Internetdelivered competitors like Netflix are not, it will be difficult for Canadian providers to compete or even to survive, especially if foreign competitors face no Canadian tax. We recommend several changes to regulatory rules to address the challenges ahead. The Canada Media Fund, an important source of subsidies particularly for drama, effectively requires the recipients of those subsidies to have Canadians in all important on-screen and production roles. Changing those rules and rewarding export success, will allow more of those products to be exported. Canadian broadcasters are excluded from fully exploiting the fiction programming they commission as they are required to rely on independent producers in ways that limit the retention of ancillary rights and up-side to this content. By relaxing these rules, and giving the broadcasters more “skin in the game, ” the quality of the programming may increase. As revenues decline in the broadcasting system, the subsidy mechanisms, such as the Canada Media Fund, will need to be topped up with additional funding. One idea is requiring a subsidy from Internet service providers while reducing the subsidy broadcasters pay. However, such a tax would be overly broad, as only a portion of the Internet is used for media purposes. We think a better idea is to use the proceeds from the auction of wireless spectrum. In the US, the majority of the auction proceeds were paid directly back to broadcasters from the government, which is not happening in Canada. Finally, the government should conduct a periodic review, perhaps every five years, by a group outside of the CRTC that could determine the health and necessary reforms of the broadcasting system and its ability to support Canadian content.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".