Consumer valuation of food attributes: a comparison of willingness to pay estimates from choice modelling and contingency valuation methods
Bibliographic record
Abstract
This study compares the willingness to pay values from two different stated preference methods, choice modeling (CM) and contingent valuation (CV).The CV approach used was a multiple bounded discrete choice (MBDC) format.The WTP values were estimated for different food products that contained different environmental and health attributes.The two methods were found to generate statistically different WTP estimates for tomatoes and pork and were statistically similar for milk.The difference seems to reside in the model specification; when the attributes were analyzed as nonlinear, the WTP estimate using the CM method was statistically similar to the one estimated with the CV method.Tests on sequencing and bid ordering effects were also conducted on the CV data.These biases did not affect the estimated WTP when using the MBDC format.While CM allows more flexibility than CV modeling, CM tends to generate higher estimates when the modeling includes continuous variables.Therefore, special attention is necessary when simulating WTP values from implicit prices derived from CM results.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".