Reappraising the Efficiency of Probabilistic Patents and Prescriptions for Patent Policy Reform
Bibliographic record
Abstract
A number of alternative means of rewarding inventors, such as cash rewards and government buyouts of 2 inventions have been suggested.See e.g., Steven Shavell & Tanguy Ypersele, Rewards Versus Intellectual Property Rights, 44 J. L. & ECON.525 (2001) (proposing that government cash rewards would be appropriate in situations where R&D costs are high, that is, there is a large difference between marginal and average costs); Micheal Kremer, Patent Buy-Outs: A Mechanism for Encouraging Innovation, 113 Q.J. ECON.1137 (1998) (suggesting a system in which the government conducts an auction to determine the appropriate price it should pay the inventor for the invention); F. Scott Kieff, The Case for Registering Patents and the Economics of Present Patent-Obtaining Rules, 45 B. C. L. REV.55 (2003) (proposing a registration system with only "soft" examination procedures and a reduced presumption of validity for issued patents would be socially beneficial because it would require patentees to more carefully scrutinize prior art and apply for patent claims, in order to avoid post-issuance challenges for validity or infringement). I.2 patent enforcement could be more efficient and, whether stronger patent protection provides greater
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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 | 0.049 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".