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.Table 10: Control over interactions among participants Lead To Win element Does LTW council controls contractual terms of interactions?Awards 1.1 External funding award No 1.2 Research award No 1.3 Global award No 1.4 Regional award No Spark 2.1 TIM Lecture No 2.1 Featured event No 2.3 Hackathon No 2.4 Brown bag lunch No 2.5 Special topics Yes Incubate 3.1 Technovation Challenge Yes 3.2 Venture Demo Day No 3.3 Opportunity review Yes 3.4 Workshop No 3.5 Sprint No 3.6 Innovation in classroom Yes 3.7 Coach No Accelerate 4.1 Nicol internship Yes 4.2 CLA stipend Yes 4.3 Coach No 4.4 Desk Yes 4.5 Workshop No 4.6 Market Intelligence Service No 4.7 Partner showcase Yes 4.8
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".