Bibliographic record
Abstract
An examination of how advocates for alternative agriculture confront “science-based” regulation of genetically engineered crops. Genetic engineering has a wide range of cultural, economic, and ethical implications, yet it has become almost an article of faith that regulatory decisions about biotechnology be based only on evidence of specific quantifiable risks; to consider anything else is said to “politicize” regulation. In this study of social protest against genetically engineered food, Abby Kinchy turns the conventional argument on its head. Rather than consider politicization of the regulatory system, she takes a close look at the scientization of public debate about the “contamination” of crops resulting from pollen drift and seed mixing. Advocates of alternative agriculture confront the scientization of this debate by calling on international experts, carrying out their own research, questioning regulatory science in court, building alternative markets, and demanding that their governments consider the social and economic impacts of the new technologies. Kinchy focuses on social conflicts over canola in Canada and maize in Mexico, drawing out their linkages to the global food system and international environmental governance. The book ultimately demonstrates the shortcomings of dominant models of scientific risk governance, which marginalize alternative visions of rural livelihoods and sustainable food production.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".