Will agricultural digitalization deliver relative advantages in quality of work, productivity, profitability, return on investments, and reliability? Perceptions of Canadian producers
Bibliographic record
Abstract
Digitalization is often claimed by agri-food actors interested in using technology and corporations to provide many socio-economic benefits for farmers. This article reports on the results of a survey of Canadian farmers (n = 852) to explore producer perceptions of whether new digital technologies 1) improve the quality of work, 2) enhance productivity, 3) increase profitability, 4) offer a reliable return on investment, and 5) are as reliable as earlier tools and technologies. Farmer respondents generally agreed that digital farm tools have certain relative advantages, but considerable skepticism and varying views persist for certain benefits. Farmers with experience using digital tools were more likely to agree to improved quality of work and reliable return on investments. Meanwhile, farmer socio-demographics (region, level of education, farm ownership ratio) partly and to varying degrees explain perceptions of some relative advantages (e.g. productivity and reliability) but not others (e.g. profitability). In the context of the findings, we note with caution that farmers’ optimism for the relative advantages of digitalization could lay the foundation for acceptance of and receptivity for these innovations. However, if the goal is to encourage producers to embrace digital innovations widely, targeted programming to increase farmers’ first-hand experiences and research-backed informational programs on the potential benefits of digitalization would be needed.
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How this classification was reachedexpand
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".