Bibliographic record
Abstract
This dissertation examines the increase in youth cultural production and youth involvement in the creative industries. The project researches structures that youth encounter, including federal, provinicial, and municipal policies, as they attempt to create small-scale and self-generated careers for themselves in the creative industries, and also looks at initiaves that youth create themselves, including artist networks, in order to facilitate their entry in the realm of work in the creative industries. After examining Canadian federal cultural policy in comparison to British cultural policy and provincial educational policy in Québec, I turn to a series of case studies concerning local, national, and international artist networks. Drawing on existing research from the sometimes disparate fields of research in creative economies, cultural studies, media education, and subculture studies, this dissertation examines the possibilities and limitations in the ways that these fields address youth cultural production. Ultimately, connections need to be made between these fields to fully encapsulate and support youth realities, as no one field offers an adequate theoretical framework to register contemporary youth activities in the creative industries. To this end, the project suggests a creative ecology model in order to register small-scale youth cultural production and the relationships between youth, employment, and sustainable community development.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".