GMO doublespeak: An analysis of power and discourse in Canadian debates over agricultural biotechnology
Bibliographic record
Abstract
It has been 20 years since Canada’s first commercially grown genetically modified (GM) crops were approved and debates over these contentious products continue to gain momentum. Literature exploring Canada’s GMO debates has yet to focus specifically on the discourse of pro-biotech public relations campaigns and anti-biotech movements. This paper helps fill this gap with an analysis of power relations regarding efforts to inform public opinion on the topic of agricultural biotechnology. This paper explores these power relations in two arguments. First, I argue that the Canadian state’s overall positive position toward agricultural biotechnology provides leverage to pro-biotech public relations, while delimiting the direction of anti-biotech campaigns. Second, I argue that the potency of pro-biotech frames are constituted and sustained by historically and culturally embedded norms and values, which adds additional challenges for anti-biotech campaigns. These findings uncover a clearer picture of the complexity of power relations within agri-biotech discourse, and the extent to which anti-biotech groups are disadvantaged in these debates.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.043 | 0.033 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".