Harmonizing the agricultural biotechnology debate for the benefit of African farmers
Bibliographic record
Abstract
The intense debate over agricultural biotechnology is at once fascinating, confusing and disappointing.It is complicated by issues of ethical, moral, socio-economic, political, philosophical and scientific import.Its vocal champions exaggerate their claims of biotechnology as saviour of the poor and hungry, while, equally loudly, its opponents declare it as the doomsday devil of agriculture.Sandwiched between these two camps is the rest of the public, either absorbed or indifferent.Biotechnology issues specific to the African public must include crop and animal productivity, food security, alleviation of poverty and gender equity, and must exclude political considerations.Food and its availability are basic human rights issues-for people without food, everything else is insignificant.Although we should discuss and challenge new technologies and their products, bringing the agricultural biotechnology debate into food aid for Africa where millions are faced with life-or-death situations is irresponsible.Agricultural biotechnology promises the impoverished African a means to improve food security and reduce pressures on the environment, provided the perceived risks associated with the technology are addressed.This paper attempts to harmonize the debate, and to examine the potential benefits and risks that agricultural biotechnology brings to African farmers.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".