MétaCan
Menu
Back to cohort
Record W4313417802 · doi:10.1111/1746-692x.12376

Prospects for Agroecology in Europe

2022· article· en· W4313417802 on OpenAlexaff
Alan Matthews

Bibliographic record

VenueEuroChoices · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsTrinity College
FundersInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
KeywordsAgroecologyAgricultureIncentiveSustainable agricultureBusinessAttractivenessContext (archaeology)Natural resource economicsFood systemsAgricultural economicsAgricultural scienceEconomicsFood securityGeographyMarket economyEnvironmental science

Abstract

fetched live from OpenAlex

Summary Agroecology is not a new concept. Its rising prominence, however, is linked to greater societal awareness of environmental pressures associated with agricultural practices, a decline in the number of farmers, the growing market power of major corporate businesses, and links between ill‐health and patterns of food consumption. In this context, agroecology is proposed as an alternative and sustainable model for agricultural production and for organising food systems. However, the uptake of agroecological practices in Europe is low. Fewer than 3 per cent of all farms have been defined as agroecological by achieving a pre‐determined threshold on all five of key management principles. Economic viability, specifically the return to labour, will be a key factor in determining the attractiveness of agroecological practices to farmers and ultimately the farming system chosen. If current market conditions and economic incentives prove to be insufficient to promote the uptake of agroecological practices, then the associated non‐market or social benefits may be key in generating appropriate rewards to farmers adopting these systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.208
Teacher spread0.193 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEuroChoicesSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207