Bibliographic record
Abstract
Genetically modified (GM) crops and the food products derived from them have been in widespread use in North America since 1995. A wide range of food products in North America and elsewhere contain GM derived products. With a slightly longer timeframe, and almost no public rejection or even debate, medical products throughout the developed world, including EU countries, have been produced using GM bacteria (especially E coli) and GM animals, such as goats. The next generation of GM crops will be drought tolerant, require less nitrogen and have enhanced nutritional profiles. During the almost 15 years of widespread use in North America and a number of other countries, no new risks have emerged. Nonetheless, SubSaharan African countries, with the exception of South Africa, continue to resist the use of GM crops. Given, the safety record of these crops, the mitigation of environmental degradation they achieve compared to traditional crops and the farming practices they require, and the higher yields of GM crops, this resistance and rejection is, on the surface, baffling. A full explanation will certainly be complex but a significant role is played by the post-colonial influence of developed nations – their governments and their non-governmental organisations. Today in all developed nations, medical applications of GM are moving forward at a rapid pace and in many countries outside Europe GM crop development and planting is advancing rapidly. In the developing world, China, India, South Africa and many countries in South America are moving forward with the use of GM crops. Once again because of the influences of old colonial powers, Sub-Saharan African countries are being left behind. It is time for African countries to take control of their destinies; it is time for them to turn their gaze and allegiance away from the developed world –especially Europe - towards their natural partners in development: China, India and South Africa. These countries are pointing the way forward, the way out of poverty, hunger and dependency.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".