Biotechnology, Sound Science, and the Foreign Agricultural Service: A Case Study in Neoliberal Rollout
Bibliographic record
Abstract
This paper addresses the ways in which policy coordination, and technical assistance and training programs operated by the United States Department of Agriculture's Foreign Agricultural Service (FAS) have helped produce, internationalize, and enforce a neoliberal approach to the regulation of biotechnology, genetically modified crops, and food safety, through the reductionist discourse of sound science. The internationalization of US standards forms a major component of US agrofood trade strategies, while the contentious nature of biotechnology within international trade makes standards harmonization an important political battleground within ongoing processes of neoliberalization. Relying on appeals to sound science that posit US regulations as scientific and objective, and therefore superior to other regulatory models, FAS facilitates the rollout of neoliberal institutions within both the US state and in developing and post-Communist countries to harmonize biotechnology and food safety standards in line with US-led neoliberalization and capitalist internationalization. I examine the political and scientific contours of the sound science discourse, and offer two examples through which FAS has incorporated and deployed sound science in undertaking neoliberal rollout—first by creating a new internal Biotechnology Group, and second through focusing aspects of its Cochran Fellowship Program, a longstanding development and training program for foreign regulators, on biotechnology issues.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.023 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".