Science Denial and Agricultural Technology Adoption: The Perspective from the Political Economy of Agricultural (mis)information
Bibliographic record
Abstract
Adoption of scientific agricultural information is a pressing need to address the food security of in the face of pressing emergencies like climate change and the COVID crisis. Researchers have recognized that public trust in governments and knowledge-producing institutions is at a historic low due to growing socioeconomic inequalities and political polarization. The rise of online misinformation has made the scientific truth about pressing issues like climate change, COVID crisis, and Genetically Modified Organisms (GMOs) as matters of perspective, hurling them in the domain of post-truth and alternative facts. In many cases, the political positions and press releases [T]rump over scientific consensus evident in documented issues like vaccine hesitancy and COVID hoax discourse. In Ontario, agricultural advisory stakeholders recognize the dire needs and challenges of catalyzing agricultural technology adoption, especially at the farm level. We interviewed 20 agricultural advisors in Ontario to understand the catalysts and deterrents impacting the adoption of agricultural technologies by farms. The findings indicate various dynamic factors influencing rejection of scientific recommendations and inhibiting the process of accelerating adoption. There is an emergence of blame casting among different stakeholders due to conflicting interests and ideological positions. The lack of unbiased information emerged as one of the central challenges deterring the adoption of agricultural technologies, which is also contributing to agricultural science denial. In this presentation, we propose a new thinking from the political economy perspective to explain the dynamics of agricultural (mis)information that provides a promising insight into the deterrents of agricultural technology adoption in Ontario, along with the social realities behind science denial. Funding: OMAFRA through the Ontario Agri-Food Innovation Alliance KTT Stream
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".