Use and Abuse of the Precautionary Principle GM Crops- How Corporations Rule and Ruin the World
Bibliographic record
Abstract
The proposal that "agricultural biotechnology is vital for the future of the developing world" can immediately be contradicted if we are talking about GM crops. Evidence is building up that they are unsafe, unsound and unsustainable. If they’re not good for us here they can’t be good for the developing world. GM crops allow corporations to tighten their monopoly on agriculture though patented seeds that farmers can’t resow. And that is especially important for the developing world. Last month, it transpired that GM canola fields in Canada had contaminated non-GM seeds sold to Europe, after tens of thousands of hectares have been planted. GM pollution is not restricted to cross-pollination between the same or related species. Prof. Kaatz of Jena has just discovered that GM genes may have jumped from GM pollen to bacteria and yeasts in the gut of baby bees [ 1]. The finding is not unexpected, as scientists including myself have been warning of this possibility for years. The risks of gene jumping are inherent to the GM technology. GM genetic material is not like ordinary genetic material. Natural genetic material innon-GM food is broken down by special enzymes to provide energy and building-blocks for growth and repair. And should the foreign genetic material get into a cell’s own genetic
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".