Information Policy and Genetically Modified Food: Weighing the Benefits and Costs
Bibliographic record
Abstract
Information Policy and Genetically Modified Food: Weighting the Benefits and Costs (di Mario F. Teisl, Julie A. Caswell) - ABSTRACT: The labeling of genetically modified foods is the topic of a debate that could dramatically alter the structure of the US and international food industry. The current lack of harmonization of policy across countries makes Gmf labelling an international trade issue. The US and Canada do not require Gmfs to be labeled unless the Gmf is significantly different than the conventional food or the Gmf presents a health concern. However, many other countries are requiring Gmfs to be labeled. This paper discusses empirical work on the sources and magnitude of benefits and costs from labeling programs. Information Policy and Genetically Modified Food: Weighting the Benefits and Costs - The labeling of genetically modified foods is the topic of a debate that could dramatically alter the structure of the US and international food industry. The current lack of harmonization of policy across countries makes Gmf labelling an international trade issue. The US and Canada do not require Gmfs to be labeled unless the Gmf is significantly different than the conventional food or the Gmf presents a health concern. However, many other countries are requiring Gmfs to be labeled. This paper discusses empirical work on the sources and magnitude of benefits and costs from labeling programs.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".