Industrial biotechnology for developing countries: The case for genetically modified biofuels in Kenya
Bibliographic record
Abstract
Attempts to diversify the energy portfolios of developed countries with green technologies have brought competition between food and fuel for crop production resources to the forefront of public policy debates. Biofuel policies in the European Union (EU) and the United States (US) mandate the long-term use of renewable energy in transportation, independent of production capacity and technical feasibility. Both the US and EU policies explicitly allow for biofuel imports and, hence, have the potential to provide developing countries with export opportunities. For example, the EU is seen as a market that could be supplied with biofuels produced in Kenya. As a result, contentious land acquisitions have been made in Kenya to make way for sugar cane and jatropha cultivation for biofuel production. One potential means of improving the efficiency of Kenya’s agricultural sector is the application of transgenic technologies. The objective of this article is to assess whether a biofuel industry could be developed in Kenya, based on the use of genetically modified (GM) feedstocks to supply the EU demand for biofuel. This article concludes that GM agriculture will improve the economic returns for those Kenyan farmers willing to engage in the production of GM biofuel crops. Key words: Barriers to trade, energy policy, genetically modified (GM) crops, international trade, land-use policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".