Assessment and statistical modeling of economic quality index based on deep learning models
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
The assessment of economic quality is of great significance in grasping the state of national economic development at a macro level. This paper focuses on exploring the assessment methods of economic quality and introducing deep learning models to improve the shortcomings of the traditional economic quality assessment in the assessment process. The economic quality assessment system is constructed from five dimensions, including economic vitality, and the MIV indicator values are improved by combining set-pair analysis and generalized regression neural network, so as to realize the automatic screening of economic quality evaluation indicators. According to the screening results of the indicators, the hierarchical analysis method is used to assign weights to the indicators, and the comprehensive index of economic quality is measured based on the results of the assignment.From 2012 to 2022, the economic quality of the 30 provinces in China shows an upward trend as a whole, and the comprehensive index of economic quality in 2022 is 0.90, which is an increase of 52.54% compared with that in 2012. The assessment results are consistent with the actual results, indicating that the method of this paper can effectively complete the measurement and assessment of the economic quality index, which is important for the study of economic quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it