An “Orphan” Creative Industry: Exploring the Institutional Factors Constraining the Canadian Fashion Industry
Bibliographic record
Abstract
Abstract In recent years, tier‐two fashion countries have been making gains in the global fashion industry, with hip young brands, buzz‐worthy fashion weeks and export‐oriented designers. The Canadian fashion industry, on the other hand, continues to fall behind and instead has experienced recent high‐profile closures of leading domestic fashion names. This paper explores why this is the case by considering a wide range of factors from a historical and institutional perspective. We argue that Canadian fashion is facing a number of systemic problems relating to wider institutional and policy weaknesses, rather than a lack of talent and know‐how within the entrepreneurs and businesses in the sector. While the fashion industry is indeed global, we argue that it is in fact national and local level factors—political, economic, and cultural—that structure and constrain the Canadian fashion industry for independent designers. Through exploring the experiences of this group of actors—entrepreneurial fashion designers—in this particular context, we not only learn about Canada as an economy but also what is needed in order to develop the fashion industry more broadly. We provide a framework for analysing the range of socio‐economic, historical, and political factors at the national level which affect the performance of the fashion sector and the operation of fashion designers as the entrepreneurial actors at the heart of the industry.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".