Does Choice Context Affect the Results from Incentive‐Compatible Experiments? The Case of Non‐GM and Country‐of‐Origin Premia in Canola Oil
Bibliographic record
Abstract
We seek to design and implement a WTP elicitation instrument closely resembling an actual grocery shopping experience. An incentive‐compatible choice experiment is used to elicit values for a non‐GM attribute and a country‐of‐origin attribute of Canola oil. The choice context is varied to assess whether revealed‐preference choice experiments are affected by choice variety. A median premium for non‐GM Canola oil is found to approximate C$0.45 or C$0.62/liter according to the choice variety context; different country‐of‐origin effects are also found as choice context varies. Hypotheses as to why these significant effects of choice contexts occur are suggested. Nous cherchons à concevoir et à mettre en application un instrument de révélation de la VDP qui ressemble étroitement aux choix que fait un consommateur lorsqu’il effectue son marché. Nous avons utilisé une méthode expérimentale compatible avec les incitations des participants afin de découvrir la valeur accordée à deux caractéristiques de l’huile de canola: non génétiquement modifiée et étiquetée selon le pays d’origine. La liste des choix est variée afin d’évaluer si les préférences révélées sont influencées par la gamme de choix. La prime médiane de l’huile de canola non génétiquement modifiée est d’environ 0,45 $CAN ou 0,62 $CAN le litre selon les choix proposés. Différents pays d’origine figurent aussi dans cette liste de choix. Nous avons formulé des hypothèses quant aux raisons pour lesquelles la gamme de choix a des effets importants.
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.
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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".