Strategic Scientific Disclosure: Evidence from the Leahy–Smith America Invents Act
Bibliographic record
Abstract
ABSTRACT We examine the impact of technological competition on voluntary innovation disclosure around the enactment of the Leahy–Smith America Invents Act of 2011 (“AIA”). The AIA moves the US patent system from the first‐to‐invent to first‐inventor‐to‐file system and induces a patent race that increases technological competition. Firms that are slow to file a patent are disadvantaged in this race. We find that focal firms with lagging patent classes strategically increase scientific publications in their lagging technology areas in an attempt to block competitors from obtaining a patent. This effect is more pronounced in technology areas where the firm has better information about their relative competitive position (proxied by greater inventor mobility), in technology classes with constraints on increasing patent filing timeliness (proxied by fewer experienced attorneys), and areas characterized by more intense competition. We find that the peers of firms with lagging classes experience greater patent filing rejections for lack of novelty and obviousness reasons after the AIA, suggesting that strategic scientific disclosure is effective.
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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 | 0.020 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".