The impact of national systems of innovation on therapeutic cloning : a comparison between the UK and China in the clinical area of diabetes.
Bibliographic record
Abstract
Since the discovery of genetic inheritance by Mendel (1890) and the identified role of DNA in cell division (Crick/Watson 1950), scientists have worked to advance stem cell technologies to treat and cure human disease. The broad techniques of therapeutic cloning are gene therapy, stem cells growth and pharmacogenetics together constitute a complex and demanding science. Each involves the alternating and growth of new cells including the use of human embryos undifferentiated cells and a potential to grow into any organ and tissue type. This work explores the national context in which stem cell science is advancing in a case study between the UK and China using National Systems of Innovation (NSI) as a theoretical structure. NSI is defined by the literature, which includes economic performance, political and legislative structure, research investment, and societal values (Freeman 1997; Fagerberg 2004). Using ethnographic and statistical analysis, it compares the effect each National System of Innovation is having on the advance of therapeutic cloning. Diabetes is chosen as the clinical model because of its global prevalence, affecting over 200m people (BHF 2004) and accounting for 9% of mortality (WHO 2002). and the prediction that it will become the world's most major non- communicable cause of death by 2025 (Atlas 2004).During this study, China experienced unprecedented economic growth underpinned by strong research investment, which is now three times the size of that in the UK (Wilsden 2006). It has a permissive social culture for stem cell research (Mann 2003), having adapted much of the European legislation (Salter 2007) with much of its research led by doctors, enabling a quicker advance of stem cell therapies to the clinic (Prescott). The UK is, in comparison, a global leader in stem cell science, having a prestigious record of achievements including the final mapping of the human genome (Goodfellow 2001), the cloning of Dolly the Sheep (PHGU 2002), and being first to legislate for such embryo research (HFA 1990). The UK's economic performance is also strong during this study, but well behind that of China, and neither does it enjoy the relaxed ethical stance of the Chinese structure. This is evidenced in its research investment, which has fallen as a proportion of GDP from 2.24 in 1990 to 1.78 in 2005 (National Statistics 2007), whereas China has increased from 0.7 to 1.31 (Wilsdon 2006).There is evidence in the literature of the importance of innovation to economic growth (OECDa 2004) and the relationship of this to GDP performance. This research explores the impact the National System of Innovation is having on the advance of stem cell research in the UK and China, using diabetes as a clinical model.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".