Investing In Academic Technology Innovation And Entrepreneurship: Moving Beyond Research Funding Through The Nsf I-Corps™ Program
Bibliographic record
Abstract
In 2012, the National Science Foundation (NSF) took ambitious steps to revisit how they invest in academic innovation and entrepreneurship. Rather than increasing financial investments in technology development, it created NSF I-Corps™, an innovation education program and nationwide innovation network for NSF-funded faculty and trainees. Since its launch, NSF I-Corps has trained over 3,000 researchers and has been adopted by nine federal agencies. This paper provides a brief history of government investment in academic innovation, including the conceptualization of the I-Corps program, as well as its goals, growth, and influence on other agencies. The primary data for the paper includes interviews from 13 key individuals involved in the launch of the program and publicly available program data. We conclude with a discussion of challenges and opportunities as I-Corps-related programs look to scale and sustain their efforts going forward. This paper offers government, university administrators, and faculty insight into alternative methods of promoting academic innovation and explores future research areas for entrepreneurial ecosystems and education.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".