Sustainable Start-Up Ecosystems in Terms of Capital Investment and Other Business Opportunities for Corporate Involvement – A Comparative Analysis of Hong Kong and Shenzhen
Bibliographic record
Abstract
Start-ups play one of the most significant roles in global economic development. The business environment or the business ecosystem is well-known in entrepreneurship. A start-up’s success level is positively correlated with the environment in which it operates. One of the supporting factors in the start-up ecosystems is corporations, which try to connect with start-up companies using corporate acceleration programs. Another one is a higher engagement of educational institutions within the start-up environment. The study is a comparative analysis of start-up ecosystems in Shenzhen and Hong Kong from a corporate initiative perspective. The study applied a triangulated approach, synergizing primary data from interviews with relevant start-up ecosystem stakeholders and secondary data to further examine and support the initial findings. The results identified and compared the development levels of the Hong Kong and Shenzhen start-up ecosystems and were sufficient to create recommendations and best practices for start-ups and corporates interested in the respective areas. The findings suggest that Shenzhen’s start-up ecosystem is more attractive for corporations than Hong Kong’s within the context of innovation, technology, and talent. Hong Kong will first maintain its role as a business hub and as an international asset management center and, secondly, promote the development of technologies and innovation to increase its global competitiveness. The Outline Plan positions Shenzhen as a leading innovation hub within the broader area aiming to increase the city’s level of internationalization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".