Accelerating adoption of genetically modified crops in <scp>A</scp>frica through a trade liability regime
Bibliographic record
Abstract
Given the apparently unbridgeable divide that has developed between the 25 odd countries that grow and trade GM crops and the evolving EU regulatory hurdles, it may be time to consider alternative strategies for realizing a global market for agricultural products. Africa is one area of the world where the battle over GM agriculture is being played out, yet it is the continent where GM could have the greatest positive impact. Numerous African nations, given their long-standing trade connections to European nations, fear that allowing the commercialization of GM crops could lead to comingling of GM and conventional products and, hence, the loss of export opportunities to the EU. These are legitimate concerns. One potential solution that warrants serious consideration would be to establish a pool of funds that could be accessed by African agricultural commodity exporters in instances where exports to Europe are rejected. A production levy could be imposed in leading industrial adopting nations (i.e., Australia, Canada and the United States). The revenue raised would provide an endowment fund that could be used to offset the costs arising from import refusals. African-sourced shipments rejected by the EU will most certainly have alternate markets, but could receive a reduced price or incur higher costs associated with serving alternate markets. The intent of the fund would be to compensate for the real difference between the net returns contracted with European importers and the final market price received. This article examines the feasibility of establishing such a fund and discusses the funding options.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".