Communities living near the Yamuna: Understanding their knowledge & perception of the natural environment
Bibliographic record
Abstract
The specific river discussed within this paper will be the Yamuna River in India. Unfortunately, due to intensive human intervention, the Yamuna River has also become one of the most degraded rivers in India, imposing significant effects on the socio-economic networks within surrounding villages. This paper aims to understand the knowledge and perception that communities who live near the river and depend on intensive farming have in regards to the natural environment. Contributing to a socio-economic impact assessment this research will serve as a key component to a proposed river restoration project that is being studied by researchers at the University of Guelph and the Jawaharlal Nehru University in India. Determining the villagers' understanding about the natural environment, this paper tries to determine how their level of knowledge impacts their understanding and appreciation for the proposed restoration project. Research methods that were used to gather data included secondary data analysis, participant observation, focus group interviews, household questionnaires, and semi-structured key informant interviews. The analysis of the study finds that although the majority of respondents from the villages studied appeared to have valuable indigenous knowledge in regards to the natural environment, they appeared to lack in-depth knowledge about environmental processes and concepts which affect their communities. The paper concludes by the identification of appropriate environmental education and awareness initiatives that have the potential to ensure community involvement in water conservation efforts in order to mitigate the negative impacts on the Yamuna River. Based on the findings presented in this study, "next steps" for future research that build upon the current work have also been identified.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".