Labelling Genetically Modified Food: Heterogeneous Consumer Preferences and the Value of Information
Bibliographic record
Abstract
One facet of public debate associated with genetically modified (GM) food focuses on labelling policy for products derived from GM processes. This paper reports on the analysis of effects on consumers' choices of pre‐packaged sliced bread under different GM food labelling policies. Substantial heterogeneity is found to exist among consumers' tastes for various bread attributes, including the presence/absence of GM ingredients in bread products. A simulation‐based bias‐adjusted measure is applied to estimate the value of information, as opposed to the value of the presence or absence of GM ingredients, revealed to consumers by different labelling procedures for the GM attribute. The information that is provided in a mandatory labelling context is considerably more valued by consumers than the information provided in a voluntary labelling context. In a final section, estimated consumer benefits from labelling policies are expressed in terms of average market prices for bread products, providing a measure of benefits against which potential cost increases that may be associated with labelling policies may be compared in the context of any future benefit–cost analysis of GM labelling.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".