Navigating the Policy Landscape: Strategic Responses of Innovative Firms
Bibliographic record
Abstract
Public policies, legal rulings, and government programs provide a vital backdrop for the innovative activities of firms. Although government initiatives are often launched with great fanfare and praise, their effects on innovative activity remain a matter of ongoing controversy and debate. Public policies may also affect innovative activity in ways that are indirect and difficult to disentangle. This presenter symposium assembles three studies that provide new and complementary vantage points for probing the effects of public policies on the strategic responses of innovative firms. Although the papers share a common focus on policies in the U.S. context, they feature different levers aimed at managing innovation at the international, domestic (cross-state), and regional levels. With a prominent strategy scholar as a discussant, our aim is to shed light on commonalities and contrasts across the studies and the generalizability of the findings. Acquisition Market Thickness and R&D Investments: Evidence from National Security Restrictions Author: John McKeon; Boston University Author: Zeyang Xue; Boston University Author: Rosemarie Ziedonis; Boston University Enhanced Employer Intellectual Property Rights: Effects on Inventor Mobility and Startup Creation Author: April Burrage; Stanford University Drivers of Firm-Government Engagement for Technology Ventures Author: Lauren Lanahan; University of Oregon Author: Iman Hemmatian; California State Polytechnic University-Pomona Author: Amol M. Joshi; Wake Forest University Author: Evan Johnson; University of North Carolina at Chapel Hill
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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 | 0.005 | 0.012 |
| 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 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".