Datafication on the Farm: An Exploration of the Social Impacts of Agricultural Big Data on Canadian Crop Farms
Bibliographic record
Abstract
New precision agriculture technologies, like GPS, sensors, remote sensing and drones, are transforming farming practices across Canada. These digital technologies can generate vast amounts of diverse and disparate agricultural Big Data about microclimates, yields, soil composition, and many other parameters on the farm. Agricultural Big Data platforms have emerged in order to help farmers perform analyses on these data, often to more precisely understand field conditions and manage inputs. Through surveys and interviews with Canadian farmers and precision agriculture industry professionals, this research interrogates the impact of these technologies. The thesis explores farmers’ perceptions of agricultural Big Data, and the ways in which benefits from these emerging technological systems may be distributed unevenly between stakeholders. Finally, it examines the ambiguous meanings of agricultural data ownership. This work contributes to critical scholarship that engages with questions regarding how agricultural Big Data technologies reinforce inequitable relationships of power in the food system.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".