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Canada's environmental farm plans: transatlantic perspectives on agri‐environmental schemes

2006· article· en· W2062435240 on OpenAlexfundaboutno aff
Guy M. Robinson

Bibliographic record

VenueGeographical Journal · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsPromotion (chess)AgricultureStewardship (theology)BusinessEuropean unionStatutory lawPlan (archaeology)Dimension (graph theory)Common Agricultural PolicyEnvironmental resource managementEnvironmental planningEnvironmental protectionGeographyEconomicsPolitical scienceEconomic policy

Abstract

fetched live from OpenAlex

Evaluation of Ontario's Environmental Farm Plan (EFP) scheme, launched in 1993, provides an opportunity for comparisons with agri‐environmental measures instituted in the European Union and other parts of North America. The EFP has a strong ‘bottom‐up’ dimension in that it is farmers’ organizations that have been central both to the scheme's instigation and to its ongoing management. This has affected the nature of the actions taken by individual farmers participating in the scheme. These actions are reviewed, especially in terms of the participants’ attitudes towards stewardship of the land, environmental outcomes, cross‐compliance measures, barriers to participation and the role of statutory regulation. Some contrasts are drawn with the greater ‘top‐down’ controls exerted in several EU agri‐environment schemes, with the latter's promotion of extensification and the changing role of farmers as ‘producers of countryside’ in a multi‐functional agricultural system. The diffusion of EFP schemes throughout Canada is noted and is cited as confirming the maintenance of fundamentally different attitudes to the development of farm‐based environmental actions compared with those adopted in the EU.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.146
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0120.007
Scholarly communication0.0100.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.003
GPT teacher head0.154
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 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

Citations45
Published2006
Admission routes2
Has abstractyes

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