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Record W2138748203 · doi:10.1186/gm442

The Human Genome Organisation: towards next-generation ethics

2013· article· en· W2138748203 on OpenAlexaff
Bartha Maria Knoppers, Adrian Thorogood, Ruth Chadwick

Bibliographic record

VenueGenome Medicine · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntellectual propertyConfidentialityAutonomyResearch ethicsHuman rightsInformed consentLawPolitical scienceBioethicsHarmonizationSociologyEngineering ethicsMedicine

Abstract

fetched live from OpenAlex

Ten years after the completion of the human genome [1], looking back over the policy statements of the Human Genome Organisation's (HUGO) Ethics Committee (EC) and of its Intellectual Property Committee (IPC) is more than just a trip down memory lane; it is the revelation of a seismic shift in the values underlying genomic research (Table 1).Founded in 1992 at the inception of the Human Genome Project, HUGO not only provided prospective scientifi c leadership on approaches to intellectual property, but also on ethical, legal and social issues.Indeed, the Statements constitute a harbinger of policy debates that persist today.In the past two decades, genetic research ethics has expanded rapidly from a domain seemingly 'ungoverned by any explicitly ethical or legal norms' , to a rich and sophisticated fi eld [2].Initially, the concerns of the EC and IPC were to ensure participant autonomy through informed consent, respect for participant privacy and confi dentiality in light of the sensitive nature of genetic information, and an equitable distribution of the burdens and benefi ts of genetic research.Th ese concerns are prevalent throughout the HUGO Statements, even as the focus of genetic ethics has shifted from the protection of individuals, families and communities, to considering the broader interests of society, and international harmonization.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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 designBench or experimental
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

Citations18
Published2013
Admission routes1
Has abstractyes

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