Eliciting Consumer Preferences for Certified Animal‐Friendly Foods: Can Elements of the Theory of Planned Behavior Improve Choice Experiment Analysis?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Models used in neoclassical economics assume human behavior to be purely rational. On the other hand, models adopted in social and behavioral psychology are founded on the “black box” of human cognition. In view of these observations, this paper aims at bridging this gap by introducing psychological constructs in the well‐established microeconomic framework of choice behavior based on random utility theory. In particular, it combines constructs developed employing A jzen's theory of planned behavior with L ancaster's theory of consumer demand for product characteristics to explain stated preferences over certified animal‐friendly foods ( AFF ). To reach this objective, a W eb survey was administered in the largest five EU ‐25 countries: F rance, G ermany, I taly, S pain, and the U nited K ingdom. Findings identify some salient cross‐cultural differences between northern and southern E urope and suggest that psychological constructs developed using the A jzen model are useful in explaining heterogeneity of preferences. Implications for policymakers and marketers involved with certified AFF are discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it