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Record W2753248813 · doi:10.1080/14636778.2017.1368374

Measuring the performance of international genomics research projects in fostering genomic capacity in the developing world

2017· article· en· W2753248813 on OpenAlexaff
Martin Hétu, Yann Joly, Konstantia Koutouki

Bibliographic record

VenueNew Genetics and Society · 2017
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversité de MontréalMcGill University
FundersBroad InstituteHarvard UniversityMassachusetts Institute of Technology
KeywordsCommercializationIntellectual propertyDeveloping countryGenomicsSet (abstract data type)Genomic medicineBusinessPolitical scienceEngineering ethicsEconomic growthComputer scienceMarketingEconomicsEngineeringBiologyGenome

Abstract

fetched live from OpenAlex

Therapeutic applications of genomic medicine are slowly finding their way into the healthcare framework of developing countries. The establishment of equitable innovation policies is a determining factor in how genomic-based therapeutic applications will evolve in these countries. In the biomedical field, the commercialization of research results has established itself as the dominant paradigm in the innovation system. However, many recent studies have demonstrated that this emphasis on commercialization and the protection of intellectual property has led to disappointing results. A growing number of stakeholders in this debate argue that it is now necessary to go beyond the commercialization of research and implement policies based on the research valorization paradigm, which supports the achievement of social as well as economic objectives. We thus propose a new set of more inclusive research performance indicators to help policymakers measure the impact of international genomics projects on developing countries.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.210
GPT teacher head0.334
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

Citations2
Published2017
Admission routes1
Has abstractyes

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