Universities and State Policy Formation: Rationalizing a Nanotechnology Strategy in Pennsylvania
Bibliographic record
Abstract
Abstract Technology‐based economic development programs have become a salient feature of the state policy landscape since the 1980s. While much research exists on the topic, little attention has been given to the processes of policy formation. State programs have moved towards high technology areas emphasized at the federal level over the past decades, and nanotechnology became one of the latest targets. This paper examines the eight‐year process through which Pennsylvania adopted a “state‐wide strategy,” culminating in the Pennsylvania Initiative for Nanotechnology. In this process, programs that responded to the interests of multiple agents came first, and a state policy was formulated after the fact. This pattern of “rationalized policy formation,” as opposed to rational policy formation, may be more common than suspected. Its strengths and weaknesses in this Pennsylvania case are discussed.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".