Quality Improvements in Public Livestock Services Delivery: Are Farmers Ready to Pay? An Inquiry in South India
Bibliographic record
Abstract
Farmers Willingness To Pay (WTP) for improving the quality of public livestock services delivery, in terms of Service Provider and Farmer Relationship (SPFR), chance of recovery from ailments and chance of conception following Artificial Insemination (AI), was assessed through Contingent Valuation (CV) in southern peninsular state of India, the Tamil Nadu State. The districts of the state were categorized as ‘Livestock Developed’ (LD) and ‘Livestock Under Developed’ (LUD) based on initial base line developed. Contingent Valuation (CV) approach and Tobit regressions were used to assess variations in the stated Willingness To Pay (WTP) values, and the probability of stating a positive WTP value for respondents who declared that they were not willing to pay. Overall, the respondents in the study area were willing to pay INR 3.91 for improving the SPFR attribute of the public veterinary centre, while they were ready to pay INR 5.84 for augmenting the chances of recovery from illness by the services of public veterinary centres. In order to benefit from improved chance of conception of their bovines after AI, the farmers were willing to pay INR 11.71. An absolute concordance on the levels of attributes and the variations in the stated positive WTP values for quality improvements was noticed. Tobit regression analyses on the improvements of all above attributes indicated that the farmers who were at disadvantaged levels of an attribute were willing to pay more compared to those at an advantaged level.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".