Bibliographic record
Abstract
Mr. Burk illustrates that federal courts have diverged along industry-specific paths when deciding patent cases. Burk highlights courts' disparate treatment of the biotechnology and computer software industries within the uniform patent statute. Due to industries' differing requirements for innovation and development, Professor Burk argues that the currently general patent statute and its incentive to innovate may be improved by tailoring it to specific industries. Burk creates a dialogue on what kinds of statutory schemes promote innovation. Citing the Supreme Court's statement in Diamond v. Chakrabarty that the patent statute is meant to cover anything under the sun made by man, Burk explains that courts are responsible for applying the patent statute to all new kinds of technology that Congress could not have fully anticipated when it passed the statute. Burk compares the U.S. approach to that of Canada, where certain new technologies will not be covered by its patent statute unless Parliament affirmatively legislates the matters. Essentially, Burk asks, what statutory scheme best promotes innovation? The author agrees with Chakrabarty in that the judiciary is the best place for this industry-specific tailoring to take place. Courts already use different policy levers in patent law, such as the obviousness and disclosure standards, to tailor to each industry. Professor Burk suggests that courts make more calculated uses of these tools to better promote innovation.
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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 | 0.045 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.022 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".