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Sustainable Agriculture: Farmers’ Perspectives on Transition to Sustainable Agricultural Practices

2019· article· en· W3010955248 on OpenAlexaffvenueabout
Paige Allen

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsViewpointsGeneral partnershipAgricultureSustainabilitySustainable agricultureBusinessSustainable Agriculture Innovation NetworkSustainable developmentInclusion (mineral)Environmental planningEconomic growthEnvironmental resource managementPolitical scienceEconomicsGeographyFinance

Abstract

fetched live from OpenAlex

The role of sustainability in Canadian agricultural production systems is a complex and evolving topic. In 2018 Canada announced the launch of a five-year Canadian Agricultural Partnership which is a $3 billion funding initiative between the federal, provincial and territorial governments. Innovation and sustainability is one of the key elements of the initiative. The purpose of this research is to increase policy discussion in relation to sustainable agriculture through the engagement of farmers in Southern Ontario. This research will help improve the sustainable policies and programs by investigating farmers’ views on the inclusion and transition to sustainable farming practices, factors influencing farmers’ decisions to make the transition, as well as identifying deficits in current sustainable policy and programming in Ontario. It is essential to develop research which is representative of farmers’ viewpoints on as they are the stakeholders directly impacted by the policies and programs which are developed and enacted.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0090.011
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 designQualitative
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

Citations1
Published2019
Admission routes3
Has abstractyes

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