Bibliographic record
Abstract
Biotechnology crop production area increased from 1.7 million hectares to 148 million hectares worldwide between 1996 to 2010. While genetically modified food is a contentious issue, the debates are usually limited to health and environmental concerns, ignoring the broader questions of social control that arise when food production methods become corporate-owned intellectual property. Drawing on legal documents and dozens of interviews with farmers and other stakeholders, Corporate Crops covers four case studies based around litigation between biotechnology corporations and farmers. Pechlaner investigates the extent to which the proprietary aspects of biotechnologies—from patents on seeds to a plethora of new rules and contractual obligations associated with the technologies—are reorganizing crop production. The lawsuits include patent infringement litigation launched by Monsanto against a Saskatchewan canola farmer who, in turn, claimed his crops had been involuntarily contaminated by the company’s GM technology; a class action application by two Saskatchewan organic canola farmers launched against Monsanto and Aventis (later Bayer) for the loss of their organic market due to contamination with GMOs; and two cases in Mississippi in which Monsanto sued farmers for saving seeds containing its patented GM technology. Pechlaner argues that well-funded corporate lawyers have a decided advantage over independent farmers in the courts and in creating new forms of power and control in agricultural production. Corporate Crops demonstrates the effects of this intersection between the courts and the fields where profits, not just a food supply, are reaped.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.213 | 0.071 |
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".