Evaluation Indicators and Development Strategies of Agricultural Revitalization for Rural Rejuvenation
Bibliographic record
Abstract
The goal of rural rejuvenation is to establish newly regenerated rural villages via economic development and beautification. However, it is necessary to engage agriculture in rural areas as a basis to reach the goal. In order to effectively promote agricultural development, the objective of this study is to develop the related indicators as evaluation criteria. A modified Delphi method is applied to develop the questionnaire. The indicators are divided into two categories: requirement and implementation evaluation indicators. This implies indicators in both sides should be considered simultaneously for effectively promoting agricultural development. There are four dimensions, consisting of twelve items, which are included in requirement indicators. The four dimensions are to (1) activate agricultural production (2) to promote agricultural marketing (3) to construct the distinguishing features of rural life and culture, and(4) to develop leisure agriculture and rural village experiences. The implementation indicators are comprised of five dimensions including 21 items. The five dimensions are (1) community factors (2) human resource factors (3) local resource surveys (4) environmental and facilities planning, and (5) government subsidies and guidance. To determine the relative importance sequence of the target evaluation indicators, the fuzzy analytic hierarchy process (FAHP) is applied to calculate the weight for each item. Then, the quality function development method (QFD) is adopted to explore the relative importance sequence of implementing indicators. Based upon the important items of evaluation indicators, this study proposes the development strategies recommended for the agricultural authority.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".