Bibliographic record
Abstract
Contemporary intellectual property theory concentrates on the cumulative and incremental nature of innovation and creation. A prevalent image depicts authors and inventors as “standing on the shoulders of giants.” This article focuses on a different type of innovation that has been largely overlooked by intellectual property theory and doctrine: innovation in the domains of science and art that breaks with convention, disputes existing paradigms, and “steps off” giants’ shoulders. I term it “non-linear innovation”. Drawing on multidisciplinary research ranging from the history of science, through network analysis of radical inventions, to studies of creativity, this article illuminates an embedded socio-cultural preference for incremental and linear novelty over paradigm-changing innovation. It then inquires whether intellectual property doctrine reflects this bias and whether the intellectual property regime can better foster non-linear innovation. The examination yields a series of counterintuitive recommendations concerning numerous patent and copyright law doctrines. More broadly, the analysis indicates that neither the “shoulders of giants” metaphor nor the opposite image of the “lone genius” adequately capture the dynamics of non-linear innovation. It further suggests that expanding intellectual property’s narrative of progress to accommodate non-linear innovation, alongside cumulative innovation, could significantly contribute to the ecosystem of innovation and creation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".