Want to go to Blockbuster? The CRTC is Faced with an Opportunity to Change its Regulations to Better Adapt to an Era of Dominant Foreign Streaming Services
Bibliographic record
Abstract
The rise of global streaming platforms like Netflix and Amazon Prime Video has disrupted traditional media landscapes, posing significant challenges for Canadian content production and distribution. This article examines the role of the Canadian Radio-television and Telecommunications Commission (CRTC) in safeguarding Canadian cultural identity amidst the dominance of foreign streaming services. It analyzes the CRTC’s current regulatory framework, which has struggled to adapt to the digital era, and proposes policy reforms to support Canadian content better. Key recommendations include deregulating domestic media companies to foster competitiveness, imposing sales taxes on foreign streaming services to level the playing field, directing tax revenues to the Canadian Media Fund, redefining Canadian content to reflect multicultural narratives, and incentivizing streaming platforms to distribute Canadian productions through procurement strategies. These measures aim to ensure that Canadian stories are created, distributed, and accessed by audiences while aligning with the CRTC’s mandate to promote national identity and consumer affordability in a rapidly evolving media environment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".