Not Good or Bad But Different: Free Markets, Subjective Preferences, and Labels for Genetically Modified Foods
Bibliographic record
Abstract
Arguments for and against mandatory labels for genetically modified foods tend to be based upon the assertion of public interest. Those who advocate against labels say GM foods are safe and carry significant social benefits, such as increased yields, reduced use of pesticides and enhanced nutritional value. Labels, they say, would discourage people from buying them. Those who favour mandatory labels emphasize health risks, environmental contamination and pest resistance as dangers of an untested technology. This article argues that the most compelling arguments for mandatory labeling are not based upon public interest, but upon consumer autonomy and market competition. Labels require no more than that producers disclose what the buyer is purchasing. They increase the efficiency of the market, meaning simply that demand and supply are aggregate reflections of individual wants and voluntary contracts. A government that genuinely endorsed consumer choice and free market values would require GM foods to be labelled whether they are harmful or not. The labelling question is not whether GM foods are good or bad, but whether they are the same or different from non-modified goods. If they are different, they require labels. If they are the same, they do not deserve patent protection.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".