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Does Choice Context Affect the Results from Incentive‐Compatible Experiments? The Case of Non‐GM and Country‐of‐Origin Premia in Canola Oil

2009· article· en· W2006324090 on OpenAlexafffundvenue
Dmitriy Volinskiy, Wiktor Adamowicz, Michele M. Veeman, L. S. Srivastava

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsBC Research (Canada)University of Alberta
FundersAlberta Crop Industry Development Fund
KeywordsContext (archaeology)CanolaEconomic rentEconomicsHumanitiesWelfare economicsAgricultural scienceMathematicsMicroeconomicsPhilosophyChemistryGeographyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.192
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2009
Admission routes3
Has abstractyes

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