Perceived Appropriateness of Information and Beneficiary Characteristics under Kisan Mobile Sandesh
Bibliographic record
Abstract
Research was conducted in Lakhimpur Kheri district of Uttar Pradesh to assess the role of Kisan Mobile Sandesh (KMS) in providing agricultural information to farmers. An ex-post-facto research design was adopted, and a total of 117 registered beneficiaries were randomly selected from 12 villages across two purposively chosen blocks. Data were collected using a structured, pre-tested interview schedule and analyzed with the help of percentages. Findings revealed that the majority of beneficiaries were young (58.12%), educated up to higher secondary level (46.15%), and dependent solely on farming (70.94%). Nearly half (49.57%) had medium landholdings and more than half (55.56%) belonged to families with more than five members. Most respondents fell into the medium annual income group (47.01%). Regarding psychological and behavioral variables, a higher percentage of beneficiaries reported high perception towards KMS (45.30%), medium cosmopoliteness (49.57%), high economic motivation (43.59%) and high information-seeking behavior (41.88%). With respect to message appropriateness, 45.30% of beneficiaries considered the information appropriate, 32.48% most appropriate and 22.22% less appropriate. Differential perception analysis further indicated that beneficiaries with higher perception were more likely to rate messages as appropriate or most appropriate. Overall, KMS was found to be an effective ICT-based extension tool in bridging knowledge gaps, improving access to timely information and supporting informed decision-making in agriculture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".