MétaCan
Menu
Back to cohort
Record W2087035258 · doi:10.2202/1941-6008.1052

Alternative IP Mechanisms in Genomic Research

2008· article· en· W2087035258 on OpenAlexfundaboutno aff
Cheryl Power, Ed Levy, Emily Marden, Ben Warren

Bibliographic record

VenueStudies in Ethics Law and Technology · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
FundersGenome Canada
KeywordsCommercializationIntellectual propertyAgency (philosophy)GenomicsInterimResearch programScience policyPolitical scienceTechnology transferBiologyGenomeEngineering ethicsPublic administrationKnowledge managementSociologyGeneEngineeringComputer scienceGeneticsSocial scienceLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.491
GPT teacher head0.386
Teacher spread0.105 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueStudies in Ethics Law and TechnologySame topicIntellectual Property and PatentsFrench-language works237,207