Bibliographic record
Abstract
Over the past twenty years, the Canadian television landscape has come to increasingly resemble the market-driven television of the United States, the United Kingdom and Australia, to name only the other major English-language industries. Sports, reality TV, and sci-fi drama dominate, and the public elements of the system are increasingly under siege. How did this happen? A look back over the decisions of the past two decades makes it apparent that Canada’s regulatory agency the CRTC has repeatedly enabled the system we now see. These changes are the direct result of NAFTA (the North American Free Trade Deal, signed in 1994), which drastically altered the cultural industries in Canada and led to an entrepreneurial approach to television. Since then, there has been a concerted shift toward an export-oriented industry, provoking a new emphasis on the global trade of cultural products (Edwardson 2008). In effect, even before the impact of the Internet, as the cable dial expanded, and sponsorship was diluted, production costs were pushed down and new, cheaper formats were created. At the same time, ownership became more consolidated and the telecommunication industry merged with the broadcast industry hoping to cash in on the promises of digital and wireless technologies. The CRTC enabled these shifts with the stated intention of increasing Canadian television’s competitiveness at an international level (CRTC 1999).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".