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Record W2148559137 · doi:10.1111/cjag.12003

Transfer Efficiency Analysis of Margin‐Based Programs

2013· article· en· W2148559137 on OpenAlexaffvenueabout
James Rude, Alan P. Ker

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of GuelphUniversity of Alberta
Fundersnot available
KeywordsProduction (economics)IncentiveTransfer paymentPaymentMoral hazardMargin (machine learning)BusinessGovernment (linguistics)Differential (mechanical device)Gross marginAgricultureEconomicsAgricultural economicsPublic economicsFinanceMicroeconomicsMarket economy

Abstract

fetched live from OpenAlex

AgriStability, Canada's major farm support and business risk management program, has been in place since 2007. As with most agricultural insurance programs, AgriStability creates opposing incentives where moral hazard and misallocation effects discourage production while the risk reduction effects encourage production. We investigate the relative size of these effects to determine both the degree to which production is distorted and the percentage of government transfer that remains with the producer. Our results indicate mild but differential effects across crops. We find roughly 45% of program payments remains with primary producers. These findings are of particular interest because of their World Trade Organization implications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.150
Teacher spread0.134 · 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.

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

Citations18
Published2013
Admission routes3
Has abstractyes

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