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Record W4361002126 · doi:10.1016/j.oneear.2023.03.004

Against the odds: Network and institutional pathways enabling agricultural diversification

2023· article· en· W4361002126 on OpenAlexaff
Jennifer Blesh, Zia Mehrabi, Hannah Wittman, Rachel Bezner Kerr, Dana James, Sidney Madsen, Olivia M. Smith, Sieglinde S. Snapp, Anne Elise Stratton, Mohamed I. Bakarr, Abram Bicksler, Ryan E. Galt, Lucas A. Garibaldi, Barbara Gemmill‐Herren, Ingo Graß, Marney E. Isaac, Innocensia John, Sarah K. Jones, Christina M. Kennedy, Susanna Klassen, Christian Levers, Laura Vang Rasmussen, Claire Kremen

Bibliographic record

VenueOne Earth · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversity of British Columbia
FundersH2020 European Research CouncilHorizon 2020European CommissionNational Science FoundationEuropean Research CouncilHorizon 2020 Framework ProgrammeNational Socio-Environmental Synthesis Center
KeywordsDiversification (marketing strategy)SustainabilityAgricultureBusinessAgricultural diversificationFood securityIncentiveNatural resource economicsAgency (philosophy)Environmental resource managementBiodiversityEnvironmental planningEconomicsEcologyGeographyBiologyMarket economy

Abstract

fetched live from OpenAlex

Farming systems that support locally diverse agricultural production and high levels of biodiversity are in rapid decline, despite evidence of their benefits for climate, environmental health, and food security. Yet, agricultural policies, financial incentives, and market concentration increasingly constrain the viability of diversified farming systems. Here, we present a conceptual framework to identify novel processes that promote the emergence and sustainability of diversified farming systems, using three real-world examples where farming communities have found pathways to diversification despite major structural constraints. By applying our framework to analyze these bright spots in the United States, Brazil, and Malawi, we identify two distinct pathways—network and institutional—to diversification. These pathways emerge through alignment of factors related to social and ecological structure (policies, institutions, and environmental conditions) and agency (values, collective action, and management decisions). We find that, when network and institutional pathways operate in tandem, the potential to scale up diversification across farms and landscapes increases substantially.

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)0.002
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0070.010
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.067
GPT teacher head0.218
Teacher spread0.152 · 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

Citations61
Published2023
Admission routes1
Has abstractno

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