Socio-cultural barriers to developing a regional entrepreneurial ecosystem
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
Purpose The purpose of this paper is to provide a contextual analysis that helps explain how socio-cultural factors are negatively impacting the evolution of the entrepreneurial ecosystem in a struggling regional economy. Design/methodology/approach A case study method is used to provide a detailed contextual analysis triangulating primary and secondary data. Findings This paper provides insight into a region impeded from embracing the benefits of innovation-driven entrepreneurship in fostering economic development. The authors show that socio-cultural factors may be inhibiting the region from having a functional entrepreneurial ecosystem that can support innovation. Specific aspects of culture and social capital weaknesses are identified and insight into the potential causes of these impediments were offered. As well, the paper shows how the fundamental nature of culture may be affecting other elements of the entrepreneurial ecosystem from maturing. Originality/value This paper adds to a small, but growing, body of literature that is illustrating the evolutionary nature of entrepreneurial ecosystems and the significant impact of socio-cultural attributes to that evolution. This paper responds to calls to investigate contexts in which innovation does not thrive and where economic challenges prevail. The value of this research paper is to provide conceptual contributions in a contextual analysis from which other researchers can draw comparisons, insights and inspiration for similar approaches. Despite the abundance of research discussing the importance of culture, there are very few actual case studies showing concrete examples of culture and its influence on a region’s entrepreneurial ecosystem.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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