R&D performance evaluation and analysis under uncertainty: the case of Chinese industrial enterprises
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
Abstract Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D performance under uncertain conditions. Using this approach, we analyze the R&D performance of industrial enterprises across 30 Chinese provincial regions from 2018 to 2022. The empirical results reveal a notable decline in R&D performance during 2018–20, driven by external shocks such as trade conflicts and the pandemic, followed by a gradual recovery post-2020, a trend that remains consistent under varying levels of data perturbation. Regional analysis highlights substantial disparities in R&D performance across Chinese regions. Comparative analysis further demonstrates the proposed model’s advantages in feasibility and computational efficiency. Based on the empirical analysis, we provide several policy implications. While rooted in the Chinese context, this paper contributes both methodologically through its robust DEA framework for handling uncertainty, and empirically by offering valuable insights into improving R&D performance in diverse national and organizational settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.114 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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