Bibliographic record
Abstract
Following the recent ‘green light’ from the FDA in the US and from the EFSA in the EU, cloned meat and milk may soon arrive on food shelves. Yet evidence suggests that consumers remain apprehensive about eating such food, for reasons which stand outside the scientific community (including ethical and moral issues). These consumer concerns may be hard to accommodate. Not least, in the US no labelling will be required. Although a more restrictive legislative approach is envisaged in the EU, this could trigger a WTO dispute. If the Dispute Settlement Body of the WTO declared cloned and conventional foods to be ‘like products’, commercial advantages for the US would ensue, as cloned products would be allowed to be sold onto the markets with no labelling, creating one market combining conventional and cloned foods. In particular, the application of the principle of non-discrimination could oblige the EU to remove any labelling of cloned products. On the other hand, consumer preferences are a factor to be considered in any ‘like product’ determination and it may be that the consumer hostility towards cloned food in the EU may prove decisive.
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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.026 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".