Fostering the Business of Innovation: The Untold Story of Bowers v. Baystate Technologies
Bibliographic record
Abstract
Perhaps the law review literature does not need another essay on the Federal Circuit’s Bowers v. Baystate Technologies case. That case has received more than its share of attention from commentators, all criticizing Judge Rader’s majority opinion and most extolling the virtues of Judge Dyk’s dissent. Despite the storm of scholarly criticism, however, courts have followed Judge Rader’s opinion. This essay tells the untold story of why courts have been wise to do so. The essay explains how commentators have argued that federal intellectual property law should have preempted Bowers’ claims for breach of a shrinkwrap license prohibition on reverse engineering. Instead, Judge Rader’s majority opinion eliminated Bowers’ copyright claim by refusing to award Bowers any remedies for copyright infringement and hinted that in many instances contract damages for breach of a prohibition on reverse engineering would be de minimus. By using remedies rather than federal law preemption, Judge Rader’s approach achieved a result that was fairer to the parties and more congruent with sound innovation policy and the business of 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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".