Models of Technology Transfer for Genome-Editing Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it