Bibliographic record
Abstract
Drought is one of the leading environmental challenges, with its social and political aspects relatively underexplored in the Middle East, particularly in countries like Iran. Characterized by its arid and semi-arid climate, Iran has been grappling with drought for approximately 30 years, with projections suggesting this challenge will persist for another two decades. This study examines how drought, as a critical issue, and its diverse consequences—particularly political ones—are portrayed within Iranian society. It also attempts to (i) analyze variations in drought impacts across the country and (ii) gain deeper insights into its political and social dimensions. The findings reveal that the political and socio-economic impacts of drought have intensified significantly in recent decades. This study underscores the importance of integrated water management strategies, sustainable agricultural practices, and active civil society engagement. It concludes that addressing the drought and water crisis requires a comprehensive approach that integrates regional, social, and political perspectives, fostering sustainable development and consistent policies in Iran. The study compares two provinces in terms of the consequences of and responses to drought, highlighting both similarities and differences. In both regions, it is noted that the political dimensions of drought—such as local mobilization—are increasing, and politics is becoming more engaged with environmental issues. Moreover, the rise in environmental problems like drought has contributed to a decline in political trust in government policies on water. Regarding the differences, the study indicates that political engagement around drought and water issues is more prominent in Isfahan than in Hamadan. Findings show that there is a decline in the capabilities of government on water governance, and the political mobilization on the water issue differs among provinces. The study suggests a need for more localized and regional studies on drought and water issues across the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".