Satisfaction Levels of Insured Apricot Producers towards Agricultural Insurance Services
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
The objective of this research was to assess satisfaction levels of the insured farmers towards TARSİM agricultural insurance services. The study was conducted on the farmers engaged in apricot production in Malatya province of Turkey, the world’s largest provider of apricots. About 69.88% of Turkey’s dried apricot production and about 73.44% of the world dried apricot production are based in Malatya. Face-to-face interviews were conducted with a random sample of 187 farmers. Likert scale questionnaires were used to collect opinion data of farmers on five dimensions, namely sales and marketing, damage compensation, pricing and payment policy, customer notification and customer representation. Structural equation modeling was used to explore the association between the measured variables and overall satisfaction levels. The results of structural equation modeling indicated that all dimensions had statistically significant effects on farmer satisfaction. Additionally, according to the standard estimations, satisfaction from sales and marketing, satisfaction from customer notification and satisfaction from damage compensation were the most significant determinants of customer satisfaction. Pricing and payment policy had the lowest influence on farmer satisfaction. The study results showed that efficient and rapid resolution of farmer problems and grant of ease for premium payments were the most influential factors affecting farmer satisfaction.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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