Mapping the entrepreneurial ecosystem of the cultural and creative industries: an examination of the Corner Brook region, NL, Canada
Bibliographic record
Abstract
This thesis examines the application of the Entrepreneurial Ecosystem Mapping (EEM) framework developed by Stam and Van de Ven (2021), to the Cultural and Creative Industries (CCIs) in Corner Brook, Newfoundland and Labrador, Canada. The research aims to assess the current state of the area's CCIs entrepreneurial ecosystem, evaluate its sustainability, and identify opportunities for fostering creative entrepreneurship in this small, resource-constrained region. By combining secondary data analysis with insights from the researcher's embedded experience, the study provides a comprehensive understanding of the ecosystem's elements, interactions, and challenges. The findings demonstrate that Corner Brook's CCIs ecosystem is emerging with significant strengths, including robust cultural and natural assets, educational infrastructure, and local champions for creative initiatives. However, challenges like limited CCI-specific policies, funding gaps, talent retention issues, and lack of data, hinder its growth. The study proposes an adaptation to Stam and Van de Ven's (2021) EEM framework to better reflect the dual cultural-economic nature of CCIs, and to specifically integrate cultural value, natural capital, and tourism as key elements of the CCIs entrepreneurial ecosystem (Throsby, 2000). This research contributes to entrepreneurial ecosystem theory by demonstrating not only the adaptability of the EEM framework to CCIs and small, less urban regions, but also by proposing an adaptation of the model based on the findings and specifically tailored for CCIs. It also provides actionable insights for policymakers and stakeholders, highlighting the importance of customized strategies to support creative entrepreneurship and foster sustainable regional 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".