Communicating the Benefits and Risks of Digital Agriculture Technologies: Perspectives on the Future of Digital Agricultural Education and Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
British Columbia’s food system is experiencing an emerging trend in the digitalization of agriculture, which will impact agricultural practices in the province. The rapid growth of this field has created a niche for training and education in digital agriculture and more specifically, in areas such as robotics, artificial intelligence, big data analytics, and computing. However, it remains unclear whether current educators and trainers in British Columbia are communicating both the benefits and risks of digital agriculture, and the need for an inclusive and equitable approach to digital agriculture. To understand the emerging education and training landscape in digital agricultural technologies, this exploratory study engaged in a key informant interview with 12 participants, including educators, relevant government staff, and private training consultants/practitioners in the food and agricultural sector in British Columbia. The small sample is reflective of the nascent nature of this area of research, which seeks to better understand digital agriculture from the perspectives of agricultural educators and trainers both in the public and private sectors. The study found that there is currently a lack of consideration for equity and food sovereignty in digital agricultural training and education. This is primarily due to a gap in engagement with the social aspects of digital agriculture. Without engaging critical social scientists and critical data studies, digital agriculture education, and training may be conducted in ways that do not promote responsible and ethical innovation, and are therefore counterproductive to the development of a just and sustainable food system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it