"You, me, and Mexico" : myths and archetypes of Latin America in Canadian popular music
Bibliographic record
Abstract
The Canadian music scene has always been smaller than, and in many ways, dependent on, that of its neighbour to the south, but the Canadian content rules for broadcasters that took effect in the early 1970s guaranteed a measure of independence to Canadian musicians and music companies that, along with the official Canadian policy of multiculturalism, contributed to a vibrant and original music scene. One notable aspect of the Canadian music scene is its connection with music from Latin America. Latin American music once occupied only a small niche in North American and world music, but over the past three decades it has become part of the mainstream. The North American music market has historically consisted of and supported many genres, and commercial factors have resulted in a fusion of many, if not all, of them; many songs and artists have become "crossovers" on the music scene. One well-known example is Justin Bieber's English-language cover of Luis Fonsi's hit "Despacito" (2017). Other Canadian artists have incorporated Latin rhythm, melodies, and themes into their songs, and Canada is home to a number of musicians from Latin America. Music and song have always represented social and cultural groups, and many songs embody themes and stories that create social and cultural identity; a survey of Canadian popular music shows that Canadian songwriters choose and develop themes about Latin American society and culture that reflect their own deeply held values, including admiration and respect for independence and strength of character.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.042 | 0.053 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".