How Blue Can You Get? Urban Mythmaking and the Blues in Edmonton, Alberta
Bibliographic record
Abstract
The blues is a genre of music that is rich in storytelling. Growing out of an oral tradition that has spanned generations, its influence on popular music today is undeniable. Many will have some vague recollection of some of these stories—whether related to blues figures, cities, regions, or to moments in time these are stories shared by the entire blues community, and through them, people have become familiar with the mythical importance of places such as Chicago or the Mississippi Delta to blues music. But how does a system of myth-making work in regions that do not have the luxury of being at a blues crossroads? Using the Edmonton blues scene as a case study, this thesis examines some of the stories told by people who have long called Edmonton their home and who have contributed to the mythologization of the local blues scene and turned it into an unlikely home for the blues. By employing qualitative research methodologies, such as participant observation and in-depth interviews, this study aims to understand the role that mythmaking has played in strengthening the Edmonton blues scene. To demonstrate this, the thesis first introduces the history of the Edmonton blues scene and, more generally, the city itself. It then looks at how myth has been written about by other anthropologists and popular music researchers. Finally, it shares some of the stories of important venues in Edmonton and important legends of the Edmonton scene before attempting to understand how these myths and stories have helped to carve out a space for Edmonton in the larger blues world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".