Technology maturity and high tech venture attractiveness: A model for emerging technology based economic development
Bibliographic record
Abstract
Exceptional regional economic development is fueled by Schumpeterian cycle. Further, Schumpeter's economic growth cycles are initiated by emerging technologies. However, as the name implies emerging technology based products are often not fully developed. Moreover, they are often sponsored by small firms seeking to disrupt a current industry standard technology product paradigm. These are the firms that when successful generate economic job and wealth creation in the regions they reside. These firms need resources to initiate and sustain but they are typified by lower Technology Readiness Level (TRL) technology product paradigms targeted at ambiguous markets. Such firms are often eschewed by today's funders and other resource providers. Yet, if emerging technologies are the wellspring of new Schumpeterian driven cycles of economic development and the firms that underpin that development cannot be sustained then there is cause for concern. Here, we investigate if regional economic development efforts have generated any resource support for firms which focus on emerging technology based commercialization. We do this using the case study method. We seek to understand how different regions are improving the financial entrepreneurial environment. We use secondary data research techniques to find the activities those regions are performing that assist entrepreneurial and intrapreneurial efforts in their region. We provide a first ever model for economic development based on emerging technology and technology entrepreneurship. We find to our surprise some pervasive techniques but overall little commonly in the ways regions assists these firms' efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".