Innovation systems and entrepreneurial ecosystems: Implications for policy and practice in Latin America
Bibliographic record
Abstract
This paper examines the concept of entrepreneurial ecosystems and the more established concept of systems of innovation and considers their application in Latin America, where many countries are currently experiencing political and economic upheaval. The paper finds that current entrepreneurial ecosystem literature is not directly applicable to most of Latin America, as it takes for granted features of an advanced economy, while the innovation system literature favours studies of well-functioning economies and innovation in high-technology sectors. Findings suggest that network and institutional perspectives may enrich both concepts in theoretical and analytical term and complementary innovation system and entrepreneurial ecosystem perspectives appear well suited in further defining the needs and demands of local production structures and existing resource and knowledge capabilities. The paper suggests the need for measurable transformations in Latin American production and support structures that include embracing social, organisational, and interactional innovation and socially oriented entrepreneurial activity. The paper encourages further research to identify the drivers and economic consequences of distinctive Latin American entrepreneurial ecosystems and for researchers to adopt an evolutionary perspective that acknowledges historical trajectories in different regions, where local social, political, and economic regimes will influence the trajectory and success of future innovation policy initiatives.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".