A Failure in Application for Support by National Natural Science Fund:Causes and Countermeasures
Bibliographic record
Abstract
Objective The article analyzed the causes of failed cases in Nanjing Railway Medical College in application for support by National Natural Science Fund(NNSF) and put forward some countermeasures so that the applicants and managers of research work may be enlightened.Method One hundred and thirty two copyies of application rejected by NNSF between 1995 and 1999 were analyzed statistically.Result The rejection causes included improper project(48.86%),imperfect design(21.69%) and failure in writing application(3.91%).Conclusion In order to raise the success rate in application for support by NNSF,the attention of scientific workers should be paid to building up innovative consciousness,increasing competitiveness,making the research aim,definitive,strengthening preresearch and improving application quality,and the managers of research work should be serious.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".