Holistic management approach for social-ecological systems in Iran
Bibliographic record
Abstract
Social-ecological systems (SESs) in Iran face escalating challenges and pressures due to a multitude of environmental and social factors, such as global warming, population growth, and unsustainable resources management practices. This paper presents a comprehensive review of the historical context and evolution of SESs in Iran and examines the current challenges and pressures confronting these systems. The methods utilized include literature review, case studies, framework development, analysis and synthesis, comparative analysis, integration of feedback, and Driver–Pressure–State–Impact–Response (DPSIR) framework analysis. We also discuss opportunities for enhancing resilience and sustainability in the country’s SESs, including case studies of successful management approaches. This analysis highlights the urgent need for integrated and interdisciplinary approaches to manage SES in Iran, including with community engagement and inclusive participation. Our findings develop and recommend a holistic management approach that can effectively respond to changing social and ecological conditions over time. The suggested holistic management approach involves various processes and aspects, such as stakeholder engagement, baseline data collection and monitoring, scenario planning, adaptive management strategies, and evaluation. Overall, this paper provides a valuable resource for different stakeholders, especially researchers and policymakers interested in the management of SESs in Iran and offers insights to other neighboring nations encountering similar challenges in their SESs.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".