Research on Efficiency of Allocation of Higher Education Resources in China--Empirical Analysis based on DEA-Malmquist Model
Bibliographic record
Abstract
China will take advantage of the Belt and Road decade’s opportunity to achieve further development with the increasing allocation of resources towards higher education. The study examines the allocation of resources in higher education across 18 provinces along the Belt and Road in China from 2014 to 2024 using the DEA and Mulmquist models to prove the result dynamically and statically. It simultaneously compares the disparities between the land Silk Road and the Maritime Silk Road. The study demonstrates that universities located in the provinces along the Belt and Road route have a high level of efficiency in allocating higher education resources. Nevertheless, it is imperative to enhance the efficiency of resource allocation in higher education across all provinces. As an illustration, Guangdong has the highest total factor growth rate, while Tibet has the lowest. Furthermore, there is a rapid increase in this rate from 2022 to 2024. The value reaches its maximum in 2023, experiences a steep decline thereafter, and drops below 1.03 in the same year. From 2014 to 2022, China's total factor productivity remains constant, indicating a stable stage interval. The data exhibits a pattern of oscillating growth followed by decline starting from 2022. Regarding regional disparities, the averages of all the routes of the Maritime Silk Road surpass the Land Silk Road. The persistent issue of imbalanced allocation of resources in higher education is evident, so the study focus on how to maximizing the benefits derived from high education resources allocation.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".