Meeting its Waterloo? Recycling in entrepreneurial ecosystems after anchor firm collapse
Bibliographic record
Abstract
The ‘recycling’ of people, capital, and ideas within an entrepreneurial ecosystem is a key process driving high-growth entrepreneurship. Skilled workers who leave firms after successful exits or firm collapse bring knowledge and insights that they can use to start new ventures or work at existing scale-up firms. This makes large anchor firms important actors in attracting workers who may subsequently recycle into the local ecosystem. However, there is limited empirical research on recycling into an ecosystem after the loss of an anchor firm. This paper develops a novel methodology using career history data to track recycling into ecosystems. The paper develops a study of Waterloo, Ontario, home to the smartphone manufacturer Blackberry, whose decline in 2008 represented a significant shock to the local entrepreneurial ecosystem. We find that alumni of this firm engaged in very little high-growth entrepreneurship, instead entering the ecosystem as technology employees at high-growth scale-up firms. This was aided by the region&s;s increased institutional capacity to match skilled workers with new ventures, ensuring the continued success of the ecosystem over time. These findings provide a more nuanced understanding of the role of anchor firms in entrepreneurial ecosystems and how recycling affects the dynamics of entrepreneurial ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".