The Analysis for the Scale and Efficiency of China’s Major Automotive Enterprises Based on DEA Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper uses Data Envelopment Analysis (DEA) to measure the scale and efficiency of 28 major automotive enterprises in Chinese, and the results show that at this stage, large automobile manufacturers of China are under-produced and the production is too scattered, and the overall efficiency of automobile manufacturers is low. One of the main reasons is that because of the low technical efficiency value, the technological innovation capability of enterprises needs to be strengthened. The other reason is that the low efficiency of a large number of enterprises lowers the overall efficiency level. There is a positive correlation between the scale and efficiency of automobile manufacturers. Whether it is the horizontal comparison between different enterprises (nature) or the vertical comparison between the same enterprises, all show that compared with small-scale enterprises, large-scale manufacturing enterprises not only have higher scale efficiency but also have higher technical efficiency. With the expansion of production scale, the scale of enterprises and technical efficiency have improved, which shows that for the automotive industry, compared with other factors, economies of scale is the main factor that affects the automotive industry, and not only is it reflected in the scale but also in technological innovation. Therefore, when formulating policies, the relevant departments should support the development of large-scale enterprises, encourage mergers and acquisitions among enterprises, increase R&D investment, support technological innovation, and set up a scientific market exit mechanism to reduce exit costs, such as guiding the transformation of enterprises and establish a competition mechanism for the survival of the fittest.
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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 | 0.037 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it