“I grow food, IT people do cybersecurity”: Addressing cybersecurity risks in Canada's agri-food sector
Bibliographic record
Abstract
• Farmers perceive cybersecurity as a low priority, despite growing threats. • Experts call for increased education of the farming community around cybersecurity. • Cross-sectoral collaboration can build trust in best practices around agri-food cyber hygiene. This paper presents new research on the perceptions of cybersecurity threats among Canadian producers and food system experts. The study is timely due to the increasing adoption of digital agricultural technologies, which, despite their potential benefits, also introduce risks to the confidentiality, integrity, and availability of digital systems. Notably, there has been a rise in ransomware and other cyber threats targeting farming operations. Our research, consisting of interviews with 29 farmers and 5 food system experts, along with a national survey of 167 producers, explores these threats and the associated cybersecurity landscape. The findings reveal that farmers generally view cybersecurity as a low priority, in stark contrast to experts who perceive significant and growing vulnerabilities. This paper concludes with recommendations to enhance cybersecurity practices at both the farm and systemic levels, highlighting the need for increased awareness and proactive measures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".