MétaCan
Menu
Back to cohort
Record W2073131396 · doi:10.1007/s12015-014-9504-5

Stem Cell Research Funding Policies and Dynamic Innovation: A Survey of Open Access and Commercialization Requirements

2014· article· en· W2073131396 on OpenAlexafffundabout
Maroussia Lévesque, Jihyun Rosel Kim, Rosario Isasi, Bartha Maria Knoppers, Aurora Plomer, Yann Joly

Bibliographic record

VenueStem Cell Reviews and Reports · 2014
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill University
FundersInstituto de Salud Carlos IIIUniversity of AlbertaStem Cell NetworkCalifornia Institute for Regenerative Medicine
KeywordsCommercializationLegislationIntellectual propertyBusinessContext (archaeology)Public administrationPublic relationsPolitical scienceMarketing

Abstract

fetched live from OpenAlex

This article compares and contrasts the pressures of both open access data sharing and commercialization policies in the context of publicly funded embryonic stem cell research (SCR). First, normative guidelines of international SCR organizations were examined. We then examined SCR funding guidelines and the project evaluation criteria of major funding organizations in the EU, the United Kingdom (UK), Spain, Canada and the United States. Our survey of policies revealed subtle pressures to commercialize research that include: increased funding availability for commercialization opportunities, assistance for obtaining intellectual property rights (IPRs) and legislation mandating commercialization. In lieu of open access models, funders are increasingly opting for limited sharing models or "protected commons" models that make the research available to researchers within the same region or those receiving the same funding. Meanwhile, there still is need for funding agencies to clarify and standardize terms such as "non-profit organizations" and "for-profit research," as more universities are pursuing for-profit or commercial opportunities.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.389
GPT teacher head0.513
Teacher spread0.124 · 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 designObservational
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

Citations5
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueStem Cell Reviews and ReportsSame topicBiomedical Ethics and RegulationFrench-language works237,207