Bibliographic record
Abstract
Canada, like other industrialized countries of the world, has seen its agriculture sector evolve dramatically over the past fifty years. Prior to the Second World War, Canadian society was largely composed of a large number of self- sufficient subsistence-level farming families, who for the most part, produced enough to feed themselves with occasionally, some surplus to trade with their neighbours, sell at community farmers' markets or provide to export markets. Farm households represented about one third of the Canadian population in 1941. Since the Second World War, however, dramatic improvements in technology in agriculture resulted in significant productivity gains. A smaller and smaller number of farm households operating increasingly larger, more specialized farms, with higher-than-average income, has been able to produce enough to feed Canadians and export to world markets. At the same time that the Canadian agriculture sector was being transformed, Canadian agriculture policy evolved. Early Canadian agriculture policy was concerned with finding immigrants to populate the vast empty Prairies and setting up experimental research farms across the country to develop and disseminate knowledge of new crops and production techniques adaptable to each individual region's climate (see Ndayisenga et al. (2002)). Subsequently Canadian agricultural policy evolved to ensure orderly marketing, price supports, production and yield insurance, farm income stabilization and support, and more recently, risk management. The objective of this paper is to describe the various sources of farm household income data in Canada and the gaps in data that have been identified over the past few years. The paper will begin with a description of the Canadian agriculture sector, including trends in farm household income. A discussion of the various sources of farm household data that are available in Canada will then be presented, including the approaches used. Finally, we will consider the data gaps that exist based on the experience of agricultural policy-makers and researchers while conducting policy development and analysis over the past few years.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.031 | 0.106 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".