Perceived Effectiveness and Responsibilities of the Forest Biological Disasters Control System of China: A Perspective of Government Administrators
Bibliographic record
Abstract
Forest biological disaster control (FBDC) is appealing the attention in China and even across the world, while the control system plays a pivotal role in the entire control work. The survey-based comprehensive indicators system was developed to evaluate the perceived effectiveness of the entropy weight model and the perceived responsibilities of the FBDC system of China from the perspective of government administrators at the province-, prefecture-, and county- levels. Ordinary Least Square (OLS) and Simultaneous Equations Models (SEM) were further developed to quantitatively analyze the affecting factors of the perceived effectiveness. The results indicated that the perceived effectiveness of the FBDC system in China was relatively low, with a value of 47.18 (the range is 0–100). In specific, the county level has the highest value of 48.85, while the province level has the lowest value of 42.99. The major limiting factors perceived are the insufficiency of the funds and employees. In addition, the intelligentization level, the implementation of the quarantine enforcement, the infrastructure construction, and the involvement of the local communities also need to be further improved. The salary does not positively affect the perceived effectiveness, while administrators with higher education levels and ages usually have higher salaries. Furthermore, compared with the province- and prefecture-level agencies, the county-level agencies have higher perceived effectiveness and more perceived responsibilities with higher workloads. Thus, future policies are suggested to focus on diversifying the investment sources, refining the employee recruitment and promotion system, and paying more attention to the county-level agencies. The results of this study could help to enhance the understanding of the FBDC system of China, hence improving the control efficiency and reducing the economic loss caused by forest biological disasters in China.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".