Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities
Bibliographic record
Abstract
A “digital revolution” in agriculture is underway. Advanced technologies like sensors, artificial intelligence, and robotics are increasingly being promoted as a means to increase food production efficiency while minimizing resource use. In the process, agricultural digitalization raises critical social questions about the implications for diverse agricultural labourers and rural spaces as digitalization evolves. In this paper, we use literature and field data to outline some key trends being observed at the nexus of agricultural production, technology, and labour in North America, with a particular focus on the Canadian context. Using the data, we highlight three key tensions observed: rising land costs and automation; the development of a high-skill/low-skilled bifurcated labour market; and issues around the control of digital data. With these tensions in mind, we use a social justice lens to consider the potential implications of digital agricultural technologies for farm labour and rural communities, which directs our attention to racial exploitation in agricultural labour specifically. In exploring these tensions, we argue that policy and research must further examine how to shift the trajectory of digitalization in ways that support food production as well as marginalized agricultural labourers, while pointing to key areas for future research—which is lacking to date. We emphasize that the current enthusiasm for digital agriculture should not blind us to the specific ways that new technologies intensify exploitation and deepen both labour and spatial marginalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".