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HOME GARDENS IN LATIN AMERICA: WILD FOODS IN THE MESOAMERICAN EKUARO OF P'URÉPECHAS, MEXICO AND THE ANDEAN CHAKRA OF KICHWAS, ECUADOR

2022· article· en· W4318977572 on OpenAlexaff
Tania I. González-Rivadeneira, Radamés Villagómez-Reséndiz

Bibliographic record

VenueEthnoscientia - Brazilian Journal of Ethnobiology and Ethnoecology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsComputer Research Institute of Montréal
Fundersnot available
KeywordsDomesticationGeographyContext (archaeology)Latin AmericansBiodiversityFood sovereigntyEthnologyMesoamericaDiversity (politics)AgroforestryEcologyFood securityBiologyArchaeologySociologyAnthropologyAgriculturePolitical science

Abstract

fetched live from OpenAlex

Agroforestry systems comprise important spaces for biodiversity involving traditional ecological knowledge in their management. In Latin America, within Mesoamerican region as well as Andean exists a prominent kind of agroforestry system called home gardens, distinguished by the presence of domesticated plants and animals, coexisting with wild foods. In this paper, we addressed a comparative view on home gardens between p’urhépecha ekuaro and kichwa chakra to document qualitatively the relationship between diversity of wild food and food sovereignty in Mesoamerica and Andean regions, within a context of cultural change, and to contribute to the discussion of wild-domesticated continuum related to plants in different home gardens. The ethnographic research shows three main elements: 1) that the diversity of forms of lives domesticated and wild that coexist in the home gardens form part of a food sovereignty system; 2) cultural change does not just affect home gardens in negative ways; 3) wild foods are in a very complex process of domestication in which it is difficult to define the lines between wild and domesticated. Wild food studies have to consider a broad approach to how wild food relates to human cultures.

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)0.003
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 score0.311
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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

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