Using Crunchbase to explore innovative ecosystems in the US and UK
Bibliographic record
Abstract
Innovative, high-technology activities are seen as motors of development, with knock-on effects throughout their local economies. Such activities tend to organise networks that span beyond atomized firms, creating 'ecosystems' of mutual dependence as well as competition. However, such ecosystems remain poorly understood, which in turn constrains the effectiveness of any policy response. This first-steps paper uses the unique, user-generated Crunchbase dataset to fill some of these gaps. With rich information on founders, workers, products and early stage investment activity, Crunchbase has great potential for ecosystem understanding. Like many 'big data' resources, however, Crunchbase requires cleaning and validation to make it suitable for robust analysis. We develop a novel approach to gapfill location data in Crunchbase, exploiting DNS/IP address information, and run a series of tests on a raw sample of 225,000 company-level observations covering the US, UK and Canada. We provide initial descriptive results, and set out steps for further research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".