Mediating the GM Foods Debate: Lessons from the Enduring Conflict Framework
Bibliographic record
Abstract
Critics of the commercialization of Genetically Modified (GM) foods in Canada and the United States oppose the economic and political forces that create and approve the technology: the industry that develops it and the governments that approve its use. The conventional narrative pits the concerned public, labeled "anti-GM," against the "pro-GM" interests of industry supported by business-friendly governments. Based on this binary view of the interests and motivations of stakeholders, conflict between government and industry appears minimal and regulatory frameworks for genetically engineered crops look as though they are primarily designed to facilitate those technologies. This paper refocuses the current "enduring conflict" in GM food politics as a problem primarily between industry and government, not exclusively between the public and industry/government.
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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.083 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".