Agricultural support policy in Canada: What are the environmental consequences?
Bibliographic record
Abstract
This paper reviews annual government spending on Canadian agriculture that attempts to stabilize and enhance farm incomes. Over the past 5 years, 2/3 of the $3 billion spent on agriculture went into stabilization programs to support farm incomes. However, this level of support raises questions about the environmental consequences of enhanced agricultural production. Environmental impacts from agriculture are well known and addressed in US and EU policies. In contrast, Canadian government expenditures on environmental initiatives in agriculture, as a share of farm income, are more than 10 times smaller than those in the US and the EU. Nonetheless the evidence is that Canadian programs have modest impacts on production, but that chemical and fertilizer input use may be higher than in the absence of the program. One possible course of action is to introduce cross-compliance between program payments and environmental objectives. However, there are no requirements that Canadian producers receiving support comply with environmental standards. While cross-compliance could be considered in the Canadian context, policies that directly target specific environmental issues in agriculture may have greater impact.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".