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Record W1577559855 · doi:10.22004/ag.econ.28602

NORTH AMERICAN AGRICULTURAL POLICIES AND EFFECTS ON WESTERN HEMISPHERE MARKETS SINCE 1995, WITH A FOCUS ON GRAINS AND OILSEEDS

2002· preprint· en· W1577559855 on OpenAlexaboutno aff
Bruce L. Gardner

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2002
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsWestern hemisphereAgricultureAgricultural economicsProduction (economics)EconomicsAgricultural policyAgricultural productivityInternational economicsInternational tradeGeographyMacroeconomics

Abstract

fetched live from OpenAlex

This paper reviews and analyzes agricultural commodity support policies of the United States and Canada since 1995. This is a period of major changes in U.S. policies. The 1996 FAIR Act opened up new possibilities in farm policies, and subsequent debate about those policies culminated in 2002 with a new farm bill whose consequences may be quite significant. In Canada the period is more a consolidation of major changes in policy that had been made earlier. In both countries, the main issues addressed are the extent of income transfer to producers, and the market distortions created by those transfers. In particular, supply response to subsidies influences the productionconsumption balance, commodity prices in the North American market, and, through price transmission, commodity prices as well as trade flows elsewhere in the world. The paper reviews the available data and research findings on the extent of these effects. The first section recounts the recent history of the policies. The second section addresses the effects of the policies on commodity prices and trade flows. That section is focused on U.S. policies for the grains and oilseeds. The third section provides a prospective analysis of the commodity titles in the recently enacted 2002 U.S. Farm Bill

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.193
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2002
Admission routes1
Has abstractyes

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