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Record W3107366141 · doi:10.1186/s40795-020-00395-y

Promoting traditional foods for human and environmental health: lessons from agroecology and Indigenous communities in Ecuador

2021· article· en· W3107366141 on OpenAlexafffund
Ana Deaconu, Geneviève Mercille, Malek Batal

Bibliographic record

VenueBMC Nutrition · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchInternational Development Research Centre
KeywordsAgroecologyIndigenousFocus groupFood securityAgricultureConsumption (sociology)GeographyAgricultural productivityFood systemsDiversity (politics)AgroforestrySocioeconomicsEnvironmental healthBusinessMedicinePolitical scienceMarketingEcologyEconomicsBiologySociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: The displacement of traditional dietary practices is associated with negative nutritional consequences for rural Indigenous people, who already face the brunt of both nutritional inadequacies and excesses. Traditional food (TF) consumption and production practices can improve nutritional security by mitigating disruptive dietary transitions, providing nutrients and improving agricultural resilience. Meanwhile, traditional agricultural practices regenerate biodiversity to support healthy ecosystems. In Ecuador, Indigenous people have inserted TF agricultural and dietary practices as central elements of the country's agroecological farming movement. This study assesses factors that may promote TF practices in rural populations and explores the role of agroecology in strengthening such factors. METHODS: Mixed methods include a cross-sectional comparative survey of dietary, food acquisition, production and socioeconomic characteristics of agroecological farmers (n = 61) and neighboring reference farmers (n = 30) in Ecuador's Imbabura province. Instruments include 24-h dietary recall and a food frequency questionnaire of indicator traditional foods. We triangulate results using eight focus group discussions with farmers' associations. RESULTS: Compared to their neighbors, agroecological farmers produce and consume more TFs, and particularly underutilized TFs. Farm production diversity, reliance on non-market foods and agroecology participation act on a pathway in which TF production diversity predicts higher TF consumption diversity and ultimately TF consumption frequency. Age, income, market distance and education are not consistently associated with TF practices. Focus group discussions corroborate survey results and also identify affective (e.g. emotional) and commercial relationships in agroecological spaces as likely drivers of stronger TF practices. CONCLUSIONS: Traditional food practices in the Ecuadorian highlands are not relics of old, poor and isolated populations but rather an established part of life for diverse rural people. However, many TFs are underutilized. Sustainable agriculture initiatives may improve TF practices by integrating TFs into production diversity increases and into consumption of own production. Agroecology may be particularly effective because it is a self-expanding global movement that not only promotes the agricultural practices that are associated with TF production, but also appears to intensify affective sentiments toward TFs and inserts TFs in commercial spaces. Understanding how to promote TFs is necessary in order to scale up their potential to strengthen nutritional health.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.256
Teacher spread0.190 · 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

Citations43
Published2021
Admission routes2
Has abstractyes

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