Migrant Farmworkers' Experiences of Agricultural Technologies: Implications for Worker Sociality and Desired Change
Bibliographic record
Abstract
This mixed method study situated in Ontario, Canada, investigates how migrant farmworkers’ experiences with agricultural technologies (agtech) affect their attitudes, conditions, and expectations of work, and how workers envision technologies serving as supportive interventions. Through a survey and interviews, we identify that surveillance and tracking agtech (chequeadoras) affect workers, imparting negative health and safety consequences. Workers’ interactions with chequeadoras reveal three major impacts: performance expectations engender stress, surveillance causes fears of disciplinary action, and performance tracking heightens competition. These impacts demonstrate how chequeadoras erode workers’ capacity to build sociality and solidarity. In response to these impacts and to support workers’ desired workplace changes, which aim for safer environments with technical skill development opportunities, we examine tactics from HCI, critical design, and migrant justice movements. Our findings lead us to contemplate what qualifies as agtech and how we may support marginalised workers with divergent opinions regarding workplace technologies, and desired collective change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".