Decomposing the Farmer's Share of the Food Dollar
Bibliographic record
Abstract
Abstract The Canadian farm share for five crop‐based products and seven livestock‐based products from 1997 to 2010 is calculated using a supply chain IO analysis. Significant differences exist in farm shares across food commodities with higher farm shares for livestock products and lower farm shares for grain‐based products. The decline in the Canadian farm share for food consumed at home is driven in large part by the food purchasing habits of consumers. This paper also addresses the hypothesis that the decline in the Canadian farm share could be partially driven by rising input costs in post‐farmgate processes or rising input costs that have greater impact on downstream sectors than primary agricultural producers. Three experiments were conducted to assess the impact of an increase in the cost of corn, energy, and farm labor would have on commodity output prices, farm returns, food expenditure, and farm share. In all three cases, the overall farm share increases, albeit by a small amount, suggesting that these shocks have a larger relative impact on the prices of agricultural commodities than the prices of marketing commodities used in post‐farmgate activities. A two‐period comparison of these simulations shows that energy (corn and farm labour) price shocks would have had a greater (lower) impact on the farm share in 2007 than 1997.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".