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Record W2913444825 · doi:10.1080/03670244.2019.1570179

The Agroecological Farmer’s Pathways from Agriculture to Nutrition: A Practice-Based Case from Ecuador’s Highlands

2019· article· en· W2913444825 on OpenAlexafffund
Ana Deaconu, Geneviève Mercille, Malek Batal

Bibliographic record

VenueEcology of Food and Nutrition · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversité de Montréal
FundersInternational Development Research Centre
KeywordsAgroecologyAgricultureEmpowermentSustainable agricultureBusinessFood systemsFood sovereigntyAgricultural productivitySocial capitalAgricultural economicsAgroforestryEconomic growthNatural resource economicsFood securityGeographyEconomicsSociologyBiologySocial science

Abstract

fetched live from OpenAlex

Agroecology is increasingly recognized as a sustainable production strategy that is appropriate for the rural poor. Meanwhile, agricultural initiatives have received much attention for their role in improving farmer nutrition, and three key pathways between agriculture and nutrition include consumption of own production, income and women's empowerment. In this study based in Ecuador's Imbabura province, we used qualitative methods to explore the practices of agroecological farmers with respect to these three key pathways. Results demonstrate the heterogeneity of lived experiences through which agroecology increases agricultural diversity and builds social and human capital to improve nutrition. We further identify barter as an under-explored means to nutrition outcomes, and we discuss the role of the complex rationales that mediate farmers' performance on agriculture-for-nutrition pathways. Finally, our results illustrate agroecology's potential to spread nutrition-promoting practices through endogenous farmers' networks.

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)0.004
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: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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

Citations50
Published2019
Admission routes2
Has abstractyes

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