The Empirical Research on Independent Innovation Competencies of Enterprise R&D Departments
Bibliographic record
Abstract
The strength of the independent innovation competencies of enterprise R&D departments (hereinafter referred to as ERDD) decides the market competitiveness of enterprises. It is of vital importance to evaluate the innovation competencies of ERDD. Given to the characteristics of ERDD in China, this paper builds the evaluation index system of the independent innovation competencies of ERDD which is based on factor analysis and aimed to understand the corresponding strength of industrial enterprises of the whole country and every province and city clearly. In this paper, 16417 ERDD in Jiangsu province are analyzed. Firstly, their overall situation and dynamic changes of quantities, innovation input and output, platform stimulating effect are analyzed. Secondly, the dynamic changes of constituent elements on their independent innovation competencies are researched. Thirdly, the comparison and evaluation of their independent innovation competencies are carried out by the results of 31 provinces' ERDD in mainland China.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".