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Meat Demand under Rational Habit Persistence

2006· article· fr· W2071694163 on OpenAlexvenueno aff
Chen Zhen, Michael K. Wohlgenant

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2006
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsHabitHumanitiesWelfare economicsEconomicsSociologyPhilosophyPsychologySocial psychology

Abstract

fetched live from OpenAlex

The objective of this paper is to explore the theoretical implications of a meat demand model with rational habit formation. The impact of food safety information on meat consumption is systematically analyzed. Important differences between myopic habits and rational habits are underscored. Both the adjustment path to the new equilibrium and new level of consumption are affected by consumers' perceptions of changes in meat quality. The analysis has implications for empirical demand estimation by incorporating consumers' expectations and use of event dummy variables rather than index measures of food safety. Le présent article vise à explorer les implications théoriques d'un modèle de demande de viande avec formation d'habitudes rationnelles. Les répercussions de l'information concernant la sécurité alimentaire sur la consommation de viande ont été systématiquement analysées. Les différences importantes entre les habitudes rationnelles et les habitudes myopes ont été soulignées. Le chemin de rajustement du nouvel équilibre et du nouveau degré de consommation est affecté par la façon dont les consommateurs perçoivent les changements dans la qualité de la viande. L'analyse a des implications pour l'estimation empirique de la demande en raison de l'intégration des attentes des consommateurs et de l'utilisation de variables fictives plutôt que de mesures indicielles de la sécurité alimentaire.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.025
GPT teacher head0.150
Teacher spread0.125 · 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.

Study designTheoretical or conceptual
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

Citations9
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicEconomics of Agriculture and Food MarketsFrench-language works237,207