Database, Methodological Tools, and Research Opportunities: Creative Destruction Lab and Early-Stage Technology Ventures
Bibliographic record
Abstract
We introduce a new dataset built from a global non-profit startup program for early-stage hightechnology startups called Creative Destruction Lab (CDL).The early stages of startup formation remain one of the least understood aspects of firm growth.The nature of this program and the data collected from its operations are well suited to investigate open questions in entrepreneurial strategy, entrepreneurial finance, advice, and technology transfer.This dataset combines three critical features for rigorous empirical research.First, the large, multi-year sample includes roughly 15,000 applicants, 9,000 founders in admitted startups, and nearly 2,000 mentors.Second, CDL recognized the academic value from the outset and built enterprise IT linking venture-level characteristics, structured longitudinal records of firm development and operations, and unstructured verbatim transcripts of mentor-founder discussions.Third, the data cover 27 technological domains such as therapeutics and quantum computing.This paper provides an overview of the setting from which data is collected, a high-level description of available data, and information on how to access these data for research.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".