Failure’s virtues: the seeding of an emerging entrepreneurial ecosystem in a peripheral region
Bibliographic record
Abstract
A key strategy of governments in economically-lagging regions is to provide financial support to innovative start-ups. Yet, if such businesses fail, governments are criticized for ‘wasting tax-payers money’. This paper challenges this narrative. It provides a case study of Consilient Technologies, located in St John’s, Newfoundland and Labrador, Canada’s most economically under-developed province. The company had developed technology for the emerging cellular-phone market. It received significant funding from the Federal and Provincial governments. It recruited talent who would have left the province to seek employment and attracted others back to the province. It provided its workforce with the opportunity to acquire new competences, experience and knowledge along with entrepreneurial learning. Following its closure, its employees either moved to other technology firms in the emerging local entrepreneurial ecosystem or started their own businesses. The company also had significant demonstration effects for aspiring technology entrepreneurs. The study demonstrates to policy-makers that businesses that they support but which subsequently fail can nevertheless have a positive impact on the ecosystem. Accordingly, they need to have need to a tolerance for failure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".