Investing In Academic Technology Innovation And Entrepreneurship: Moving Beyond Research Funding Through The Nsf I-Corps™ Program
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
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.
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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.075 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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