Going over-the-top: reassessing Canadian cultural policy objectives in a converged media environment
Bibliographic record
Abstract
The Canadian media landscape is changing at an unanticipated pace, catching public and private broadcasters off-guard and ill equipped to meet the changing demands of the market. This is placing significant strain on the regulator's existing approach to new media regulation. Since 1999, the Canadian Radio-television and Telecommunications Commission (CRTC) has employed a policy of non-regulation-or as I will argue in this paper, a policy of non-policy regarding new media broadcasting undertakings (NMBUs). While NMBUs cast a wide net in terms of what we would classify under this term, its most widely known proponents-"over-the-top" (OTT) providers like Netflix Inc., Hulu, Apple TV, and countless others are taking the lion's share of the criticism and concern in Canada by broadcasters and social groups like ACTRA, and the Canadian Media Production Association (CMPA). This paper will provide an environmental scan of the existing approach to new media regulation in Canada by examining The Broadcasting Act, the New Media Exemption Order (NMEO), and the OTT Fact-Finding Mission (and results). This exploration will identify existing policy gaps, provide a history of the regulatory model, and highlight a brief case study on the Office of Communications (Ofcom) in the United Kingdom that has adopted an umbrella regulatory model that may be useful when exploring new options for new media policy in Canada. Finally, it will identify some existing roadblocks for undertaking such a policy review by looking specifically at the legislative confines of The Broadcasting Act.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.045 | 0.021 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".