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Record W4416776062 · doi:10.53555/b6f8fs92

People Participation In Community Works And Their Drought-Related Problems In The Man River Basin

2023· article· W4416776062 on OpenAlexvenueno aff
Mr. Navanath Damodar Bandgar, Prof. Dr.S.T. Kombde

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedGovernment (linguistics)Community participationDrainage basinCommunity developmentAgricultureWatershed management

Abstract

fetched live from OpenAlex

This research paper examines the role of community participation in drought mitigation and assesses villagers’ perceptions of government drought relief programs in the Man River Basin. Utilizing primary data collected from 400 respondents across the Satara, Sangli, and Solapur districts in 2024, the study evaluates awareness, effectiveness, and impact of key government initiatives such as the Integrated Watershed Development Program (IWDP), Jalyukt Shivar Abhiyan (JSA), Indo-German Watershed Development Program (IGWDP), NABARD Holistic Watershed Development Programme, and others. The study also investigated the socio-economic challenges faced by rural populations during droughts, including water scarcity, crop failure, fodder shortages, unemployment, and health risks. Findings revealed that there are sharp disparities in community participation and program effectiveness across districts of the Man river basin. The study also found significant structural vulnerabilities which are persisting in Man river basin. The study arrived at conclusion that there is need of strengthening local governance, enhancing community engagement, and improving communication strategies to foster drought resilience.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.275
Teacher spread0.118 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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