Configuring the new digital landscape in western Canadian agriculture
Bibliographic record
Abstract
Digital technologies are working to transform the global agricultural system. Farmers and firms are creating, adapting and adopting a range of new hardware, software, mobile apps, sensor technologies and big data applications, which is working to disrupt established structures within the farm machinery and associated data sectors. Focusing just on the extension of precision technologies to agriculture, this paper maps the competitive landscape using a 2 × 2 typology that situates entities operating in Canada based on their strategies, distinguishing between top-down and bottom-up networks of competitors and collaborators and the degree of interoperability of their digital applications. We examine the emergence of four specific cases in western Canadian agriculture. The typology and the cases suggest global agri-food firms, industry collectives and a host of entrepreneurial start-ups and small and medium-sized enterprises are competing to both organize and disrupt the global agri-food value chain. It is not yet clear which strategy, if any, will prevail and provide the model for broad acre agriculture in Canada and around the world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".