How Can Accelerators in South America Evolve to Support Start-Ups in a Post-COVID-19 World?
Bibliographic record
Abstract
The COVID-19 pandemic has affected the world in drastic ways, disrupting the normal operation of the world's economic activity. Every aspect of life as we know it has changed. The business and entrepreneurship landscapes have been deeply altered. As innovation intermediaries support entrepreneurship, accelerators have become progressively prominent in the entrepreneurial ecosystem of several countries. Their development is on an upward trajectory. However, literature is scant on this newer acceleration phenomenon, particularly in some regions. Furthermore, literature on the effects of the pandemic on accelerators is non-existent. In recent years, the acceleration model has grown rapidly in South America. In this rapid response paper, we build from current literature, trends and expected post-COVID-19 scenarios to investigate how accelerators in South America will need to evolve to support start-ups in a post-COVID-19 world. We developed a conceptual model, the Post-COVID-19 World Accelerator Model, to guide business accelerator managers, researchers, policymakers and entrepreneurs. We conclude by offering future research areas urgently needed to further our understanding of emerging trends affecting accelerators and start-ups in what will be a very different business landscape post-COVID-19.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".