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Record W3122468991

Consumer Preference for Eggs from Enhanced Animal Welfare Production System: A Stated Choice Analysis

2013· article· en· W3122468991 on OpenAlexaboutno aff
Yiqing Lü, John Cranfield, Tina M. Widowski

Bibliographic record

Venue2013 Annual Meeting, August 4-6, 2013, Washington, D.C. · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsPreferenceWillingness to payAnimal welfareConsumer choiceEconomicsConsumer demandMarginal utilityCageLiving spaceWelfareProduction (economics)BusinessDiscrete choiceFree accessRevealed preferenceMicroeconomicsPublic economicsEconometricsDemographic economicsBiologyEcologyComputer scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

The first choice experiment investigated consumer preferences for different housing systems, while the second investigated consumer preferences for attributes of a housing system. Each choice experiment had two information treatments. In both treatments, a description of each housing system was provided, while in the second treatment, there was additional information regarding the consequences (in terms of effect on birds) of each housing systems based on scientific research. The results indicate that Canadian consumers are willing to pay a premium for eggs from free run and free range systems, but not for eggs from enriched cage systems. There are also positive marginal WTPs for cage-free, outdoor access, access to nests box, perches, scratching pads and more space. In both choice experiment, the WTP for enhanced animal welfare attributes are lower in treatment 2 (with additional information) than treatment 1. Consumer preference for outdoor access is quite consistent across two choice experiments.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.042
GPT teacher head0.237
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venue2013 Annual Meeting, August 4-6, 2013, Washington, D.C.Same topicEconomic and Environmental ValuationFrench-language works237,207