Bibliographic record
Abstract
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".