Alternative IP Mechanisms in Genomic Research
Bibliographic record
Abstract
This research is conducted by the Intellectual Property and Policy Research Group at the W. Maurice Young Centre for Applied Ethics at the University of British Columbia. It is part of the GE3LS (ethical, environmental, economic, legal and social issues related to genomics research) component of the Genome Canada Project "Dissecting Gene Expression Networks in Mammalian Organogenesis," MORGEN, which is located principally at the British Columbia Cancer Agency, Vancouver, British Columbia, Canada. The project is involved in upstream, basic genomic research. Part of this work includes the characterization of gene regulatory mechanisms governing organogenesis with a special focus on the heart, liver and pancreas. This paper serves as an introduction to both the MORGEN case study and the role of alternative mechanisms, such as open source. We discuss interim research results as they relate to our broader study of the relationship between open science, commercialization and technology transfer offices. The role of technology transfer offices (TTO) is central to our analysis and is viewed as a key factor in implementing Genome Canada policies and principles associated with IP and 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 | 0.048 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".