Canada’s Audience Massage: Audience Research and TV Policy Development, 1980–2010
Bibliographic record
Abstract
In Canada, audience research has been a useful link between the needs and wants of audiences and the type of television content they receive; this link is made manifest in part by approximately $200 million (Cdn.) invested in research as part of the $20 billion-plus media industry.1 Canadian media and advertising firms employ precise social scientific methods and the latest tools to ensure ever-more accurate measurement of media behaviour and of public opinion. Canada was among the first to adopt the Portable Peoplemeter (PPM), developed by Arbitron in the United States but extensively tested for the first time in Montreal, ten years ago (Savage, 2006). Canadian citizens, governments and corporations have been among the most eager in the world to adopt new communication technologies: in the past five years, Canadians have embraced the web 2.0 environment — including interactive digital media production and social networking.2 All of which is meant to put Canadian audiences at the centre of media production and distribution, so that programming can be edited and created in a fashion more reflective of the audience’s identity and preoccupations.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".