An Empirical Study on the Impact of Machine Learning-based Information Resource Management System on Regional Economic Development Patterns
Bibliographic record
Abstract
The construction of information resource management system is a promotion for upgrading industrial structure and enhancing independent innovation capability.Based on the city-level data of a region from 2010 to 2022, the evaluation index system of information resource management system and regional economic development mode is established, and the variables are measured according to the collected data using gray correlation analysis method.Then double machine learning method is applied to explore the influence effect of information resource management system on regional economic development model.The baseline regression analysis reveals that the information resource management system can promote the regional economic development model, with a regression coefficient of 0.029, and the conclusion still holds after the stability test.The heterogeneity results show that regions with better economic foundation (0.067) and peripheral cities (0.036) are more significantly affected by the positive spillover effect of the information resource management system.This paper combines machine learning algorithms with traditional causal inference to explore the role path of information resource management system to promote regional economic development model, which provides empirical evidence and decision-making reference for promoting regional economic development.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".