Perceived Appropriateness of Information and Beneficiary Characteristics under Kisan Mobile Sandesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it