Bibliographic record
Abstract
Abstract In the 21st century, it is widely understood that, to make a living in jazz, Canadian musicians must either take a day job or seek out commercial opportunities in the United States. But it wasn’t always so. Until 1980, Toronto was home to a thriving music industry, driven in large part by a vibrant advertising and film music business. Indeed, far from leaving Canada for greener pastures to the south, musicians (including some Americans) were moving to Toronto. As U.S.-born musician Tom Szczesniak noted, “The streets were paved with gold.” This story complicates broadly accepted jazz discourses in a number of ways. Since it is based in Toronto rather than canonical U.S. jazz centers, it asks readers to re-examine common assumptions about North American jazz geography. As it reveals the relationship between jingles and jazz to be essentially symbiotic (at least for a time), it forces readers to rethink the presumed antagonism between jazz and commerce. Finally, as the story moves into the 1980s, it offers a vivid glimpse into the profoundly deleterious impact of neoliberal business practices and government regulations on social networks among musicians (including the union), and the musical work that sustains them.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.029 |
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".