Adaptation, Evolution, and Key Performance Factors of Canadian Business Incubators and the Impacts of Covid-19
Bibliographic record
Abstract
Canada, with its vast land and diverse population, has been developing its own entrepreneurial ecosystem and some business incubators and accelerators (BIs) have been attracting international attention. Boundaries between incubators and accelerators and among the types of BIs have been blurring. All three levels of governments are playing a leading role in the BI realm, but they are increasingly partnering with the private sector. The world of BIs in Canada is becoming less dichotomous; it is not either public or private, for profit or non-profit, or regional or international. The three most important performance factors for BIs are mentoring and coaching, building internal community, and offering external networks to the clients. COVID-19 created havoc among the entrepreneurial stakeholders, but most including BIs regained their strength by the second year of the pandemic. The ecosystem players adopted virtual platforms quickly, which will be the lasting impact of the pandemic.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".