Consumers’ Willingness to Pay For Organic Beef In Cagayan Valley
Bibliographic record
Abstract
This study on “Consumers’ willingness to pay for organic beef in Cagayan Valley region” aimed to analyze the consumers’ willingness to pay premium for organic beef in Isabela, Quirino, Nueva Vizcaya and Cagayan. Data was obtained through “face to face” interview with 407 sample respondents using semi-structured questionnaire adopting “Contingent Valuation Methodology” (CVM) and subjected to multivariate Logistic Regression analysis. Descriptive statistics like arithmetic mean, standard deviation, percentages, and ranking were employed to describe the socio-economic characteristics of the respondents. Five level Likert Scale and the Chi-Square test were used to depict the consumers’ awareness of organic products and to measure the goodness of fit of the data, respectively. Majority of the respondents were females, married, with an average age of 47.22, and belonged to household size of 4.7. The respondents were predominantly Roman Catholics, Ilocanos, and had attended formal school. Most of them were government employees earning Php20,000–Php24,999 per month and a quarter belonged to various categories of occupation. Analysis of the consumers’ perception regarding conventional and organic beef in consideration of the given variables revealed that the perceived attributes are significant. Given the price choice scenario, majority favored the minimum price of Php340/kg. The respondents are willing to pay premium for organic beef if they feel that the price is reasonable and that they get value for their money. The most important attributes of organic products that have greater influence to consumers’ WTP premium for organic beef are price, quality, health factor, and use of synthetic chemicals.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".