Technology maturity and high tech venture attractiveness: A model for emerging technology based economic development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it