Marketing GM Roundup Ready rapeseed in Norway? Report from a value workshop
Bibliographic record
Abstract
During the spring of 2007 the secretariat of the National Committee for Research Ethics in Science and Technology (NENT) organised a so-called ‘value workshop’ discussing the ethical acceptability of marketing GM Roundup Ready rapeseed. As this rapeseed has been approved for marketing in the EU the Norwegian authorities must now decide whether to allow it in Norway. The Norwegian Gene Technology Act demands that ’the production and use of genetically modified organisms […] take place in an ethically justifiable and socially acceptable manner’. The value workshop is a methodology that is thought to be suited for the task of assessing ethical justification. It involves using an ethical matrix method to structure ethical dialogue with affected parties to an issue. Relevant affected parties are e.g. Canadian farmers, Norwegian farmers, consumers, industry, the animals targeted for this feed, as well as the environment. Individuals representing these different parties and interests (of obviously different character), as well as other interested individuals and experts, were invited to discuss ethical principles, factual consequences and value trade-offs involved in accepting this product on the market. A conclusion on ethical acceptability was sought, but it turned out that all the available time was consumed in identifying the values and discussing the relevant facts. A final balancing and conclusion on the ethical justifiability of marketing GM Roundup Ready rapeseed in Norway was therefore not reached. However, the workshop did result in important input for further decision making.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".