Bottom‐up Triple Helix: science policy in the states of the USA
Bibliographic record
Abstract
Purpose The paper aims to investigate the emergence of science policy in the states of the USA, drawing attention to the fact that every state has a science and technology agency and multiple programs that attempt to raise the level of science and technology in the state and attract resources from elsewhere. Design/methodology/approach The paper builds upon the authors' previous study of high‐tech growth and renewal in Boston and Silicon Valley through analysis of documents and interviews with key actors in universities, S&T policy units of the Governor's association to detail the bottom‐up initiatives exemplifying the US innovation policy model. Findings The path dependent elements in US science and technology policy are an enhanced role for universities, an ambivalent role for national government and industry and a growing role for state and local government. Federal research funds, largely confined to support of agricultural research before the Second World War, became available for a variety of civilian and military purposes, on an ongoing basis, after the war. An assisted linear model of coordinated innovation mechanisms has been constructed on this base to translate inventions into economic activity through university‐industry‐government interactions. Originality/value The paper shows that S&T policy at the state level fills gaps in university‐industry relations, leverages federal R&D spending and enhances local comparative and competitive advantage.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".