Innovation finance ecosystems for entrepreneurial firms: A conceptual model and research propositions
Bibliographic record
Abstract
This paper presents a conceptual model for examining the matching of entrepreneurial firms and sources of finance under conditions of innovation. The model applies to entrepreneurial ecosystems, typically the site for studying venture capital investment in technology start-ups. By delimiting ecosystems along sectoral and temporal dimensions, the model considers a diversity of industry sectors and a diversity of funders, across the early, growth, and mature stages of a firm. The conceptual model suggests that where there might appear to be funding gaps, there could instead be overlaps. Based on the sectoral and temporal dimensions, research propositions are formulated. These propositions outline a research agenda that examines the relationship between innovation and sectoral and temporal dimensions, the impact of innovation on firms and funders, the role of reinvestment into the ecosystem as the lifeblood for a self-reproducing healthy ecosystem, and the possible geographic limitations of ecosystem models.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".