Bibliographic record
Abstract
Abstract This article addresses the ongoing impact of Canadian Content Regulations as applied to commercial radio. While commercial broadcasters have repeatedly called for a relaxation of the regulations in response to the changing music industry, particularly the increased impact of the Internet, it is possible to demonstrate that the regulations have had a positive impact on Canadian listening habits. An examination of the ‘national’ charts provided by Last.fm, a website that tracks users’ listening habits, shows that Canadian users listen to Canadian tracks in excess of the amounts currently regulated for radio. Commercial broadcasters’ claims that the regulations prevent them from competing fairly with new technology thus run counter to such evidence. As official charts, and hence commercial playlists are still reliant on older modes of tracking music, via in-store purchases, an incomplete picture of the current state of the industry exists, and it is this picture that seems to shape the claims made by the commercial industry. Additionally, this paper explores the rise of a successful Canadian ‘scene’, spearheaded by bands such as Arcade Fire and Broken Social Scene, that demonstrates the impact of policy in creating a national music culture that is confident enough to no longer have to be explicitly Canadian, either sonically or lyrically. Cancon regulations would appear to have aided in situating Canadian acts comfortably within a wider music culture within Canada.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".