Strategic planning for sustainable local development in Iran using the Meta-SWOT model
Bibliographic record
Abstract
A prerequisite for creating sustainable development is the emphasis on learning and adaptation across diverse geographic levels. Strategic plans, created to identify a local model of sustainable competitiveness in terms of economic, social, and environmental factors, make it possible to identify the basic form of collaborative management. Accordingly, this research explored sustainable local strategic planning using the Meta-SWOT model in the village of Yingijeh, Marivan County, Iran. The study used purpose sampling, and data were collected through interviews with experts in the field. The Meta-SWOT results clearly reveal: the significant and determining effects of ‘sustainable local development’ in Yingijeh; planning and investment in both the public and private sectors; rural residents’ involvement in development planning and implementation; officials’ focus on integrating policy and management in the area of sustainable local development; and attention to the development of Lake Zrebar, subsequently encouraging tourism growth in the study area. This implies valuable, unique, and irreplaceable factors compared to other factors studied, necessitating special attention to them. According to the results of our overall assessment, the village requires strong integrated management, a clear vision, and explicit rules. Research findings show the important impact of economy on environmental sustainability.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".