Government‐enterprise collusion and public oversight in the green transformation of resource‐based enterprises: A principal‐agent perspective
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 In this work, by constructing a principal‐agent model, we analyze the intrinsic causes of collusion between the government and enterprises, particularly through the central and local governments and resource‐based enterprises. The analysis has been conducted by introducing the public as a third‐party monitoring body to explore the positive role of public participation in preventing collusion between the government and enterprises, and henceforth entailing model analysis and validation with certain examples. The green transformation of resource‐based enterprises is an effective way for their sustainable development, besides being an inevitable requirement for China's high‐quality economic development and ecological civilization construction in the new era. In this perspective, our study reveals that: (1) Government‐enterprise collusion is motivated by the central government's improper assessment and incentive mechanism, besides the information deficit between the central government and the colluding parties. (2) The conditions for government‐enterprise collusion in development remain on the resource‐based enterprises and local governments that face fewer expected penalties than expected benefits, thus resulting in lower collusion risks. (3) Public participation in monitoring can effectively combat the willingness of the local governments and resource‐based enterprises to collude and significantly increase the level of effort of both parties in the green transition. (4) Public monitoring increases the probability of collusion detection, and prompt detection improves the timeliness and effectiveness of punishment. The findings from this study can provide a scientific basis for improving the regulatory system, thus improving public participation and strengthening the penal system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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