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Record W1546763332 · doi:10.1787/5kgch21wkmbx-en

Distribution of Support and Income in Agriculture

2011· paratext· en· W1546763332 on OpenAlexaboutno aff
Catherine Moreddu

Bibliographic record

VenueOECD food, agriculture and fisheries working papers · 2011
Typeparatext
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureDirect PaymentsIncome SupportPaymentFarm incomeEquity (law)Transfer paymentAgricultural economicsProduction (economics)Gross incomeDistribution (mathematics)Economic inequalityContext (archaeology)BusinessIncome distributionPopulationGovernment (linguistics)EconomicsEuropean unionComprehensive incomeInequalityPublic economicsGeographyEconomic policyFinanceWelfareMarket economy

Abstract

fetched live from OpenAlex

Agricultural production and support in Canada, the United States, and the European Union are highly concentrated on larger farms, which have higher income levels than the average of all farms. Smaller farms, though, are more dependent on support (in particular, payments) which accounts for a larger share of their gross receipts. As payments to farmers are more equally distributed than production, government support reduces income inequality by farm size and farm type. This study, carried out in the context of the OECD Network for Farm Level Analysis, concludes that improved efficiency and equity of policies will require better targeting of income support and, in turn, better information on the income and wealth situation of the agricultural population.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.015
GPT teacher head0.184
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2011
Admission routes1
Has abstractyes

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