A SERIES OF UNFORTUNATE EVENTS: THE GROWTH, DECLINE, AND REBIRTH OF OTTAWA’S ENTREPRENEURIAL INSTITUTIONS
Bibliographic record
Abstract
Purpose – This chapter examines how informal and formal entrepreneur- ial institutions are influenced by economic crises. These institutions act as the foundation for many, if not all, entrepreneurial activities, but they are highly vulnerable to change during times of crisis. Design/methodology/approach – This chapter uses a case study of software entrepreneurs in Ottawa, Canada, to better understand the influence of the 2001 and 2008 recessions on the social and economic aspects of entrepreneurship. This case is examined through a set of 39 semi-structured interviews with entrepreneurs, investors, and economic development officers. Findings – While informal entrepreneurial institutions have adapted to a changing economic environment, formal institutions and government programs have so far failed to do this. This results in less effective entrepreneurship support programs. Research limitations/implications – As with other qualitative case studies, these findings are not generalizable to other regions. This chapter calls for further research is needed to better understand the social forces behind institutional change. Practical implications – This chapter argues that entrepreneurship support programs must be customized to the informal social institutions that underlie all entrepreneurial behavior and practices. This alignment potentially increases the usefulness of such programs to entrepreneurs. Originality/value of the paper– While entrepreneurship in Ottawa has been carefully studied, there has been very little work examining how technology entrepreneurship in Ottawa has fared after the decline of the telecommunications market. This chapter is useful to both entrepreneur- ship scholars as well as practitioners and policy makers interested in how entrepreneurial institutions react to crises.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".