Models of Technology Transfer for Genome-Editing Technologies
Bibliographic record
Abstract
Many of the fundamental inventions of genome editing, including meganucleases, zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR, were first made at universities and patented to encourage commercial development. This gave rise to a diversity of technology transfer models but also conflicts among them. Against a broader historical and policy backdrop of university patenting and special challenges concerning research tools, we review the patent estates of genome editing and the diversity of technology transfer models employed to commercialize them, including deposit in the public domain, open access contracts, material transfer agreements, nonexclusive and exclusive licenses, surrogate licenses, and aggregated licenses. Advantages are found in this diversity, allowing experimentation and competition that we characterize as a federalism model of technology transfer. A notable feature of genome editing has been the rise and success of third-party licensing intermediaries. At the same time, the rapid pace of development of genome-editing technology is likely to erode the importance of patent estates and licensing regimes and may mitigate the effect of overly broad patents, giving rise to new substitutes to effectuate commercialization.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".