A policy framed analysis of the Valley of Death in U.S. university technology transfer
Bibliographic record
Abstract
At least as far back as the enactment of the Bayh-Dole Act of 1980 there has been an ongoing desire on the part of politicians, policy-makers and the public in the U.S., to obtain greater economic returns on the federal investment in publicly funded university research. Today among policy-makers there is an apparent belief that a capital shortage in the mid-stages of technological development is the rate-limiting factor, preventing the maximum flow of university inventive knowledge from entering the marketplace. The consequence is a Valley of Death demise for the vast majority of university inventions. In order to mitigate the problem, changes to federal granting policies are placing increased emphasis on funding more applied and translational research than basic fundamental science. Given the foregoing direction of policy, the study set out to confirm the current understanding of the Valley of Death on the part of policy-makers and relate this understanding to the historical evidence. Consistent with present-day political pronouncements, the study findings verify an overwhelming belief that a shortfall of applied research funding is the root cause of the Valley of Death. Policy-makers believe this shortfall constrains the development of basic research into commercializable products. However, the study also found that this perception is inconsistent with the empirical evidence. The study reveals a gap between these sectors but the gap is independent of the stage of technological development. A funding difference extends the entire length of the research and innovation spectrum, suggesting other factors are responsible for the adoption of university inventions, bringing into question the direction and likely efficacy of current policy initiatives. The findings lend credence to the less cited cause of the Valley of Death, namely a Darwinian Sea of survival of the economically fittest technologies (Auerswald & Branscomb, 2003). The actual stage of development of a university invention will determine the extent of investment funding necessary for its continued development, but economic factors will determine if further investment in its development is warranted. A death does exist for many inventions, but it is the result of natural market causes and not a funding shortfall, per se.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".