Progress in Human Embryonic Stem Cell Research in the United States between 2001 and 2010
Bibliographic record
Abstract
On August 9th, 2001, the federal government of the United States announced a policy restricting 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 research 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 Until the policy was repealed on March 9th, 2009, no U.S. federal funds were available for research on hESCs extracted after August 9, 2001, and only limited federal funds were available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 stimulated 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".