Bibliographic record
Abstract
This dissertation is a cultural and historical analysis of the history of popular music funding in Canada, from 1949 to 2013. Canada has an extensive public subsidy system for popular music, deriving from the implementation of content quotas for publicly licensed airwaves and the need to supply these airwaves with appropriate content. This thesis evaluates and analyses the history that engendered these policies to reveal their impact on the concept of Canadian identity as a whole, and the impact of Canadian identity formation on the popular music industries. Through six decades of policy interventionism, Canada’s popular music structure has grown into a set of structures within structures, combining direct federal subsidies, regionally specific support and private initiatives mandated by public policy. However, in this history, never has the total system been conceptually audited as a whole, nor has its underlying cultural and economic impact been analysed. Some see this support as a lifeline while others a tax, resulting in a set of structures framed on policy to simultaneously develop national cultural unity and economic prosperity. These policies have shifted from supporting Canadian cultural policy to more business driven, economic objectives, despite being mandated, legislated and ultimately beholden to Canadian cultural polices, nationalism and protectionism. By dissecting the history, this thesis aims to unravel an extensive and unique case study between the popular music industries and government as a whole.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".