Developing unicorns and gigacorns : challenges and choices for creating a purpose-driven innovation ecosystem in British Columbia. Summary report for The Leverhulme Trust
Bibliographic record
Abstract
Based on interviewees with incubators and accelerators as well as entrepreneurs from BC, this report summarises findings from a research project to develop understanding and insight into whether and how the BC innovation ecosystem is effectively designed towards steering new business activities that address complex interconnected sustainability issues.Findings suggest a strong overlap and agreement between both incubators and accelerators on the one hand and entrepreneurs on the other.In fact, themes emerging were surprisingly consistent between both sets of interviews and yet also pointed at persistent tensions and challenges.Interviewees not only identified numerous barriers and concerns but also provided a comprehensive list of ideas and recommendations for different stakeholders across the innovation ecosystem.The report concludes with five calls to action as useful starting points for further debate and consideration among all readers:1. Recognise and leverage the uniqueness of British Columbia's context as a key driver of and benefit for the wider innovation ecosystem 2. Create a purpose-driven innovation ecosystem around entrepreneurship for sustainability 3. Encourage and drive partnerships across sectors, organisations, and institutions 4. Develop and promote new models of sustainable financing that better reflect the needs of impact and purpose-driven entrepreneurs 5. Significantly address and integrate equality, diversity and inclusion questions and concerns across organisational cultures and working practices
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".