Failure’s virtues: the seeding of an emerging entrepreneurial ecosystem in a peripheral region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it