Growing Forward with Agricultural Policy: Strengths and Weaknesses of Canada's Agricultural Data Sets
Bibliographic record
Abstract
Canada has four major sources of information on the financial and production aspects of agriculture: the Census of Agriculture, the Farm Financial Survey, the Agricultural Taxation Data Program, and administrative data resulting from Business Risk Management programs. These data sets form the basis for the analysis of Canadian agricultural policy, which has shifted from a focus on farm family income enhancement, to commodity‐specific supply stabilization, to enhancing the competitiveness of the sector and individual operations. The changing focus of agricultural policy together with the growing heterogeneity of the farm sector has significant implications for the forms of analysis conducted and the suitability of the data collected for analysis. No single data set supplies all the data necessary to determine the need for and effect of farm support programs. Census data provide descriptive measures of total production but lack detailed farm financial information. Such data is provided by tax data but information is not provided on assets/liabilities, inputs/outputs, and demographics. Proposals to consider the individuals behind a unit of production in the determination of support eligibility would drive an even larger gap between data demands and the current supply of publicly provided data.
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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 | 0.057 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.055 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".