Canadian agriculture technology adoption
Bibliographic record
Abstract
Abstract Objectives Statistics Canada administers the Agricultural Census every 5 years, and this paper presents unsuppressed data from the 2016 and 2021 Census. The data set encompasses detailed information on farm types, sizes, technology choices, and a demographic profile of farm operators from the 2021 Census. Data on farm characteristics and operator demographics is crucial for understanding innovation in agriculture and formulating evidence-based policies. Data description The data sets cover the two most recent agriculture censuses of 2016 and 2021, presenting data on the number of farmers by region, farm type, size, and the adoption of technologies. Additionally, a third data set lists the number of farm operators by age and sex. The census questionnaire inquires about using different technologies, varying the types across the two census periods. Notably, there is no data suppression in these data sets, and they cover all 10 provinces in Canada, excluding the three territories. Farm types are categorized based on the North American Industry Classification System (NAICS), and farm size is measured in acres.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".