Climate-Resilient Agriculture and Migration Dynamics in Maharashtra: A Comparative Analysis
Bibliographic record
Abstract
Under the context of global climate change patterns and trends, rural India faces heightened vulnerability to changing weather conditions, leading to various shocks and stresses that can forcibly displace a growing number of its residents. This displacement exacerbates issues of poverty, food insecurity, and marginalization in these populations. A recent strategy to address this challenge is climate resilient agriculture (CRA), an approach aimed at improving community and individual resilience within the context of climate change by sustainably utilizing natural resources through crop and livestock production systems. This research project, titled the "Climate-Resilient Agriculture and Migration Dynamics in Maharashtra: A Comparative Analysis," seeks to analyze the impacts of CRA programs on migration dynamics in rural India. The primary goal is to assess the diverse designs of CRA initiatives, their implementation and governance mechanisms, and their relative effectiveness in building resilient rural development and mitigating forced displacement due to climate change. The study will entail an extensive literature review of case studies examining the effects of climate-resilient agriculture in rural India. Additionally, field studies conducted by the project supervisor, Dr. Marcus Taylor, the Department Head and Professor of Global Development at Queen’s University with expertise in agriculture, livelihoods, and anti-poverty policies in rural India and other regions, will complement the research. By identifying more sustainable adaptation approaches to address the challenges of forced migration for vulnerable populations, this research aims to contribute to improved policy responses, particularly in the Global South. The study's timeliness is emphasized by recent agro-ecological impacts in India due to extreme climate variability. Understanding the relationship between climate-induced migration and climate-resilient agriculture could have significant implications for international organizations such as the United Nations and the World Health Organization in shaping effective climate change mitigation and adaptation strategies. Ultimately, this research strives to advance knowledge in the realm of climate resilience and its impact on migration dynamics, paving the way for enhanced socio-economic outcomes and improved climate change response mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".