You get what you pay for : independent music and Canadian public policy
Bibliographic record
Abstract
The aim of this MA thesis is to demonstrate how corporate concentration within \nthe global music industry specifically affects the Canadian music industry's ability \nto compete for its own national audience as well as audiences worldwide. \nFederal public policies, regulatory regimes and subsidies are considered within \nthe context of the structure of the global marketplace which is, in effect, an \noligopoly controlled by four major corporations. Through an extensive literature \nreview of political economy theory, Canadian public policies and music studies, \nas well as personal interviews conducted with Canadian musicians, \nentrepreneurs and public servants, I will situate my research within the body of \npolitical economy theory; present a detailed report of the structure of the global \nmusic industry; address the key players within the industry; describe the \nrelationship between the major corporations and the independent companies \noperating in the industry; discuss how new technologies affect said relationships; \nconsider the effectiveness of Canadian public policies in safeguarding the \nnational music industry; and recommend steps that can be taken to remedy the \nshortcomings of Federal policies and regulatory regimes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 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".