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Record W7095154016

RESEARCH ARTICLE Progress in Human Embryonic Stem Cell Research in the United States between 2001

2016· article· en· W7095154016 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsEmbryonic stem cellGovernment (linguistics)Basic researchState (computer science)Federal fundsPaceResearch policyState government
DOInot available

Abstract

fetched live from OpenAlex

On August 9th, 2001, the federal government of the United States announced a policy re-stricting federal funds available for research on human embryonic stem cell (hESCs) out of concern for the “vast ethical mine fields ” associated with the creation of embryos for re-search purposes. Until the policy was repealed on March 9th, 2009, no U.S. federal funds were available for research on hESCs extracted after August 9, 2001, and only limited fed-eral funds were available for research on a subset of hESC lines that had previously been extracted. This paper analyzes how the 2001 U.S. federal funding restrictions influenced the quantity and geography of peer-reviewed journal publications on hESC. The primary finding is that the 2001 policy did not have a significant aggregate effect on hESC research in the U.S. After a brief lag in early 2000s, U.S. hESC research maintained pace with other areas of stem cell and genetic research. The policy had several other consequences. First, it was tied to increased hESC research funding within the U.S. at the state level, leading to concentration of related activities in a relatively small number of states. Second, it stimulat-ed increased collaborative research between US-based scientists and those in countries with flexible policies toward hESC research (including Canada, the U.K., Israel, China, Spain, and South Korea). Third, it encouraged independent hESC research in countries without restrictions.

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)0.011
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 score0.167
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.255
GPT teacher head0.486
Teacher spread0.231 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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