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Record W2477651987 · doi:10.1017/cbo9780511619564.003

Agricultural support and environmentalism

2007· book-chapter· en· W2477651987 on OpenAlexaff
John Warren, Clare Lawson, Kenneth Belcher

Bibliographic record

VenueCambridge University Press eBooks · 2007
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Saskatchewan
FundersEuropean CommissionAustralian Government
KeywordsEnvironmentalismAgriculturePolitical scienceGeographyLawArchaeologyPolitics

Abstract

fetched live from OpenAlex

Introduction This chapter outlines some of the key events in agricultural policy from the beginning of the twentieth century until the recent reforms of the European Common Agricultural Policy (CAP) in 2003, the 2002 US Farm Bill and ongoing world trade negotiations. It describes the widespread introduction of subsidies to support farm prices and the unprecedented expansion of agricultural production, to the advent of food surpluses and concerns over the environmental impact of modern agricultural practices. The development of agri-environment measures and the change in emphasis from an agricultural policy that supports production agriculture to one that supports the environment and rural development is explained and the principles behind agri-environment measures examined. Agricultural policy: the start of government intervention The regulation of agricultural markets and intervention by national governments to support farm incomes is not a new phenomenon. Throughout the course of history national governments employed various policies to support and protect agricultural production, such as the Corn Laws designed to protect British cereal farmers from foreign imports. However, it was not until the long-lasting economic depression of the 1920s and 1930s, which was also a period of agricultural depression with low commodity prices and depressed farm incomes, that national governments systematically intervened in agricultural markets to ensure the home production of food and to support their national industries.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.024
GPT teacher head0.176
Teacher spread0.151 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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