From bunny ears to smart phones : the development of broadcast technology and policy, audience viewing trends and measurement methods throughout the history of television in canada
Bibliographic record
Abstract
The following thesis explores how television content production, distribution, consumption, and audience measurement trends developed over time, and focuses on how content producers have strategized to capitalize on these trends. The objective of this thesis is to examine opportunities for new audience measurement systems that integrate digital forms of audience interaction and engagement with traditional television ratings systems, in hopes of providing producers and advertisers with a new form of ratings currency, or rather, a new standardized measurement system. This thesis examines the particular example of television broadcasting in Canada, including three case studies which break down the entire timeline of television broadcasting in Canada into three distinct periods: Analogue, Digital, and Digital Interactive. Each case study summarizes the period's broadcasting policy developments, broadcast distribution and viewing technology innovations, audience viewing trends, and audience measurement tactics. Additionally, each case study highlights interviews from two key informants associated with a significant televised talent show as an example of content production from the time period. This thesis concludes that while the Canadian television and media industry has already recognized the audience's desire to have content available any time, any place, and on any platform, third party audience measurement systems have yet to catch up. Implications of these discoveries are discussed in the conclusion, along with suggestions for further study. Finally, the author suggests a framework for developing audience measurement systems for the Digital Interactive broadcasting period.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".