"Crazy B****": Discriminatory Language, Radio Censorship, Regulation, and Enforcement Policies in Canada
Bibliographic record
Abstract
This thesis focuses on the censorship, or lack thereof, of discriminatory language on Canadian radio stations. In addition to purely discriminatory based language, this project also investigated the ways in which race, gender, sexuality, and ability were employed in popular music. Two data sets were analysed qualitatively and quantitatively to find that private and public radio stations in Canada are more likely to censor discriminatory or explicit content than their community station counterparts. Further, discriminatory language based on gender, is not only more likely to be contained in popular music, but it is also less likely to be censored in comparison to language based on racial or sexual orientation based discrimination. The first data set included 485 songs from the Billboard Hot 100 charts between 1985 and 2015. The second data set included 2818 songs from a six-month period (May-October 2015) of the top twenty charts from 27 different radio stations in Canada, including private, public, and community stations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".