Design of a Regional Venture-Creation Ecosystem by Reusing Components of Another Ecosystem
Bibliographic record
Abstract
Building a regional ecosystem to support the launch and growth of ventures is complex, time consuming, and risky. Instead of building the venture-creation ecosystem from scratch, individuals and organizations in a region may try to reuse components of an existing venture-creation ecosystem they deem to be successful in another region. This research identifies the defining features of business ecosystems, develops a framework to specify business ecosystems, and produces a design and a plan to build a regional venture-creation ecosystem in Jordan. The design adapts the goals and components of Lead To Win, a venture-creation ecosystem in Ottawa, Canada. This research is relevant to individuals and organizations building venture-creation regional ecosystems and seeking guidance on how to reuse components from ecosystems in other regions, and to researchers seeking to better understand the configuration required to attain similar goals in different regions. It also guides building a venture-creation ecosystem in Jordan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".