Brief Analysis of the Application and Funding Projects of National Natural Science Foundation of China in a Comprehensive Teaching Hospital, 2011-2019
Bibliographic record
Abstract
The National Natural Science Foundation of China (NSFC) plays an important role in supporting scientific research. A descriptive study was performed to understand the situation supported by the NSFC in a comprehensive teaching hospital during 2011 to 2019. The relevant situation was statistically analyzed during 2011-2019, including the amount of applications and grant, funding rate, and appropriation.During the past 9 years, the total funding rate of the science foundation was 14.29% (51/357), with a total appropriation of 189.485 ten thousands Yuan.In the past 9 years, the number of funding applications and the number of project approval showed an overall trend of fluctuation, with the project funding rate between 7.69% and 27.27%, and showed no significant changing trend in the funding rate during 2011- 2019 (χ2trend=0.54, P=0.464). We found that the approval rates of male (16.30%) and doctoral applicants (20.98%) were significantly higher than those of female (7.41%) and non-doctoral applicants (5.26%). Hospital need to introduce doctoral talents, increase investment in scientific research, and improve the motivation of medical staff to apply for the NSFC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".