New Media: Threat or Opportunity? Finding a Way to Balance New Media Initiatives within the Traditional Broadcasting World
Bibliographic record
Abstract
This research aggregated key elements discussed during the CRTC Public Hearing on New Media, in order to examine fundamentals for the development of sustainable business models within the new media environment. The use of a qualitative methodology allowed the progressive gathering of in-depth information. Three main data collection techniques were used to obtain the desired information. First, an observational case study focused on the new media public proceeding. Second, a thorough content analysis examined public submissions through grids in order to extract relevant data. Third, formal interviews with regulatory experts were used to access information at a more intimate level. In the context of this study five media groups were chosen for examination. This would include, private broadcasters, public broadcasters, the culture and independent producers sector, telecommunications companies and Internet Service Providers. This gave an overall view of each sector within the Canadian broadcasting system. As a result of this research, the Canadian media industries will have to make urgent changes. To begin, platform-specific content production will be vital to the overall success of the system. This would allow proper distribution, minimizing the need of reformatting the content. Accordingly, maximizing the use of content will ensure that Canadians have access to programming that reflects their realities. Most companies agreed that new online advertising funding methods were needed, that new media initiatives generated economic growth and that content ownership should be a priority for upcoming regulatory decisions.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.035 | 0.029 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".