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Funding for Chinese Collaboration

2010· letter· en· W2041712247 on OpenAlexaff
Scott X. Chang

Bibliographic record

VenueScience · 2010
Typeletter
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBusinessPolitical science

Abstract

fetched live from OpenAlex

In their Editorial “China's research culture” (3 September, p. [1128][1]), Y. Shi and Y. Rao describe an example of the rampant problems in China's research funding allocation, namely the selection of recipients for “megaproject grants.” I often hear stories about very expensive equipment left packed in hallways or labs for years without being used. The funding agencies often have very strict guidelines for using the funding on salaries, even though a research group's ability to hire the talent they need is often the most important factor in the success of the research program. China's funding strategy for overseas Chinese scientists is also problematic. As part of an Asia-wide trend, China has been trying to recruit talents from overseas ([ 1 ][2]). To attract established overseas Chinese researchers with advanced education from western countries, China has devoted billions of Chinese yuan to talent programs [such as the Thousand Talent program ([ 2 ][3]) recently established by the central government] that require overseas scholars to relocate to China to accept prestigious fulltime positions. However, many recipients of these awards cannot relocate because of practical and family obligations. China should focus instead on grants that fund collaborative research between overseas Chinese scholars and their peers in China. Collaborative programs are more cost-effective and more practical for those who cannot relocate. One such program is the Joint Research Fund (JRF) for Overseas Chinese Scholars and Scholars in Hong Kong and Macao, administered by the National Natural Science Foundation of China (NSFC). In 2006, a mere 0.7% of the NSFC budget was allocated to this worthy program ([ 3 ][4]). In 2008, the maximum grant was reduced from 400,000 Chinese yuan over 3 years to 200,000 Chinese yuan over 2 years ([ 4 ][5], [ 5 ][6]). By dedicating such a small budget to this program and others like it, China misses an opportunity to engage overseas Chinese scholars and benefit from their contributions to the country's research and education. 1. [↵][7]1. A. S. Huang, 2. C. Y. H. Tan , Science 329, 1471 (2010). [OpenUrl][8][Abstract/FREE Full Text][9] 2. [↵][10]Thousand Talent program [[www.1000plan.org][11] (in Chinese)]. 3. [↵][12]National Natural Science Foundation of China, Financial Statistics of NSFC in 2006 ([www.nsfc.gov.cn/english/11st/index.html][13]). 4. [↵][14]National Natural Science Foundation of China, Fund for Talented Professionals (NSFC, 2008), p. 17; [www.nsfc.gov.cn/english/06gp/pdf/2008/051.doc][15]. 5. [↵][16]National Natural Science Foundation of China, Funds for Talented Professionals (NSFC, 2007), p. 145; [www.nsfc.gov.cn/english/06gp/pdf/2007/031.pdf][17]. [1]: /lookup/doi/10.1126/science.1196916 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #xref-ref-1-1 View reference 1 in text [8]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DHuang%26rft.auinit1%253DA.%2BS.%26rft.volume%253D329%26rft.issue%253D5998%26rft.spage%253D1471%26rft.epage%253D1472%26rft.atitle%253DAchieving%2BScientific%2BEminence%2BWithin%2BAsia%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1190145%26rft_id%253Dinfo%253Apmid%252F20847253%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [9]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzMjkvNTk5OC8xNDcxIjtzOjQ6ImF0b20iO3M6MjQ6Ii9zY2kvMzMwLzYwMDUvNzU2LjEuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [10]: #xref-ref-2-1 View reference 2 in text [11]: http://www.1000plan.org [12]: #xref-ref-3-1 View reference 3 in text [13]: http://www.nsfc.gov.cn/english/11st/index.html [14]: #xref-ref-4-1 View reference 4 in text [15]: http://www.nsfc.gov.cn/english/06gp/pdf/2008/051.doc [16]: #xref-ref-5-1 View reference 5 in text [17]: http://www.nsfc.gov.cn/english/06gp/pdf/2007/031.pdf

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.983
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0060.003
Scholarly communication0.0080.007
Open science0.0030.016
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.1110.018

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 head0.582
GPT teacher head0.634
Teacher spread0.052 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreCommentary

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".

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Citations1
Published2010
Admission routes1
Has abstractyes

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