Farm Classification Systems for North American Agriculture
Bibliographic record
Abstract
As international agricultural markets become increasingly more integrated, internationally harmonized farm classification systems could become more useful for international comparisons of agricultural industries, as a tool for summarizing and analyzing micro-level data. Canada, Mexico, and the United States currently do not have a common farm classification system beyond the harmonized North American Industrial Classification System (NAICS), which the three countries developed and adopted shortly after the implementation of the North American Free Trade Agreement (NAFTA). While common policy themes exist among the three countries, such as competitiveness, innovation and sustainability, they have yet to be reflected in a comprehensive farm classification system. This paper compares farm structures in North America, using the NAICS and farm size. Additional classifications that are used in North America are summarized. Additional farm characteristics that could enhance the comprehensiveness of farm classification systems are discussed. Finally, data constraints which limit the ability to develop a harmonized classification system in the three jurisdictions are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".