Impact of kisan call centre in Punjab
Bibliographic record
Abstract
Access to information has become a key to decision making in modern agriculture. In order to take care of these needs of farmers, Government of India has set up twenty five Kisan Call Centers (KCC) in different states of India. The present study was designed to analyze the Chandigarh based KCC catering to Punjab state. Asample of 100 KCC user farmers and 20 nonuser farmers was studied to have an in-depth knowledge. Average number of calls to KCC per farmer were found to be 6-7 in a year. Main interest area of queries was pertaining to technical aspects .The traditional sources like fellow farmers and kisan melas were also of high importance to these KCC users. However, awareness about agriculture related websites/portal was almost lacking. Some aspects of decision making have shown positive impact of KCC like insect-pest control, disease control, variety selection, fertilizer application and weather related queries.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".