The 2002 Us Farm Bill'S Implications For Commodity Markets And Canada'S Agri-Food Sector
Bibliographic record
Abstract
This report's intent is to analyze the 2002 US "Farm Bill" to determine whether it is production and trade distorting, and how it will affect commodity markets as well as how it will affect Canadian agri-food. The objectives are to: · To explain the producer subsidy programs and how payments under these programs will be calculated; · To explain other provisions in the Farm Bill that are of interest to the Canadian agriculture and agri-food industry; · To discuss the implications of the producer subsidy programs for US producers' decisions to grow the major commodities and pulses, and the likely implications of those decisions for market prices; · To discuss the implications of other Farm Bill provisions, including trade and conservation programs, and country of origin labeling; · To discuss the implications of the Farm Bill for the current round of WTO negotiations; and · To provide some initial thoughts on how governments and firms in Canada and other countries might respond to the Farm Bill. To accomplish the objectives, we provide a thorough description of the Act and its provisions. We apply it to a fictitious 1000 acre farm in the US Midwest to show its financial consequences. We also use production costs from certain regions of the US to determine the level of incentive built into the Act.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".