Entrepreneurial Ecosystems in Cities and Regions:Emergence, Evolution, Future
Bibliographic record
Abstract
The concept of 'entrepreneurial ecosystems' has emerged as a means for theorizing and making policy-decisions concerning entrepreneurship and economic development within and across cities and regions. Entrepreneurial Ecosystems in Cities and Regions assembles original contributions from scholars across the world to provide an in-depth analysis of a concept that has the capability to capture a dynamic global economy with entrepreneurial innovation at the crux of its future development. It addresses wider issues concerning the evolution of new forms of industrial organisation. The book develops an agenda and understanding that aims to build upon the early explosion of interest within academic, policy, and practice circles by providing new and important insights that contribute to knowledge, direct future investigations, and to increase the effectiveness of research-based policy and practice. Entrepreneurial Ecosystems in Cities and Regions builds a framework for establishing a robust and sustainable concept that can help propel an understanding of how cities and regions around the world can use entrepreneurship and innovation as a catalyst for their future economic, social, and environmental development. The volume highlights the need to account for urban and regional contextual factors when determining the strength or otherwise of entrepreneurial ecosystems, and illustrates that these factors can lead to the development of entrepreneurial activity of quite a different nature across cities and regions.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".