Geospatial Evaluation of Sustainable Development: Analysing a Sample of a Successful Social Safety Net
Bibliographic record
Abstract
Sustainable Development (SD) not only ensures addressing the root cause of poverty but also helps in achieving the wellness of society. Protecting the natural resources for current and future generations is the main goal of the SD process. In recent times, developing countries have initiated social safety nets (SSNs) for poverty elimination and to achieve the SD goals through public works. The Government of India has initiated numerous development projects aimed to achieve SD and Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is one of them. The research objective of this article is to harness the power of geospatial technology for evaluating the public works under MGNREGA at a district level. The proposed research method utilizes the power of remote sensing data with a very high spatial and temporal resolution to monitor the development activities at the grass root level. Satellite based land-use maps, indices, and publicly available web based geospatial information systems have been used in this investigation to assess the changes that have occurred due to the community-level planned activities. The findings from this research confirm that MGNREGA has the potential to accrue multiple dividends at all the three pillars of SD, i.e., economic development, social development, and environmental protection. It was proved from this research that public works under MGNREGA besides providing the wage based employment to the beneficiaries resulted in improved water conservation and harvesting facilities in the study area and in return, these facilities acted as a catalyst for improved agricultural productivity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".