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Record W4232745844 · doi:10.32920/ryerson.14655921.v1

"We do not live for material things:" indigenous culture and food security in Brazil, the case of the Cinta Vermelha-Jundiba village

2021· preprint· en· W4232745844 on OpenAlexaff
Rita Simone Barbosa Liberato

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicRural Development and Agriculture
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEthnic groupIndigenousContext (archaeology)Food securitySociologyGeographyAnthropologyArchaeology

Abstract

fetched live from OpenAlex

This project is based on a qualitative analysis of the opinions of key actors involved in the construction of the indigenous village Cinta Vennelha-Jundiba (CVJ) in Brazil. The CVJ village represents a unique case in Brazil: for the first time in history, an indigenous group from different ethnic backgrounds got together and bought their own land. The research question that guided the analysis is in the context of the creation of the CVJ village: Does food play a role related to cultural reinvention and ethnic reconstruction? The purpose of this project is to explore how food has the communicative function of a bridging mechanism between the Pankararu and the Pataxo cultures in the CVJ village. The conclusions of the analysis show that the interaction between the CVJ's inhabitants is characterized by profound cultural reconstruction and ethnic reinvention, and food production and consumption are key factors in these processes.

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)0.003
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 score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.015
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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