Bibliographic record
Abstract
The foundations of the Canadian broadcasting system are pillared by its recognition of Canadian culture. The Canadian Radio-television and Telecommunications Commission (CRTC) recognizes Canadian culture by setting minimum broadcasting requirements for Canada’s English, French, Métis, and First Nation’s language content on Canadian television channels. What is left out of this system is the remaining media: the ethnic media. This research set out to understand the phases of Canadian ethnic media programming, from 2007 to 2019, through a case study of the OMNI multicultural channel and its history as a third language media company. This study has identified key CRTC broadcasting notices and public hearings for close documentary analysis which outline the direction of ethnic media broadcasting against an increasingly globalized media environment. The implications of these notices serve as a basis for evaluating how well policy and regulation serve the Canadian ethnic media audience. More specifically, the results of this research show how private ethnic media companies, such as Rogers Media, are tailoring their broadcasting schedules within the existing infrastructure; yet failing to meet the rapidly changing needs of the ethnic media audience. Audience competition for licensed programming, new media, and a globalized media environment are all evolving with technological developments that do not support Canada’s existing ethnic media programming model.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".