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Record W4226004925 · doi:10.30972/mom.125857

DERECHO DE LOS CAMPESINOS, A LA AGROBIODIVERSIDAD Y UNIFORMIZACIÓN GENÉTICA: CRÍTICA A LA LEGISLACIÓN VIGENTE SOBRE SEMILLAS Y CULTIVARES EN BRASIL Y ARGENTINA

2022· article· es· W4226004925 on OpenAlexaboutno aff
Rafaela Oliveira de Souza, Eduardo Gonçalves Rocha

Bibliographic record

VenueMomba etéva · 2022
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

El objetivo de este artículo es demostrar cómo los sistemas legales brasileño y argentino han contribuido a la mercantilización de las semillas, generando pérdida de biodiversidad y lesiones a los derechos de los agricultores. Se analizaron y compararon las legislaciones de semillas y cultivares de ambos países, demostrando cómo contribuyen a la reducción de semillas a un insumo agrícola. Autores como Juliana Santilli, Tamara Perelmuter, Philip Mcmichael y Dardot y Laval se utilizaron como principales referencias teóricas. Metodológicamente, se realizó una revisión documental y entrevistas semiestructuradas en Argentina y Brasil. Así, fue posible acceder a la legislación, las reflexiones académicas producidas en ambos países sobre el tema, así como escuchar la posición de diferentes actores de la sociedad civil, del mercado y del Estado sobre la problemática estudiada. El artículo, en un primer momento, señalará las similitudes entre la legislación de semillas en Brasil y Argentina. Luego demostrará cómo comprometen la agrobiodiversidad. Al final, las entrevistas mostrarán cómo la actual legislación sobre semillas está ignorando los derechos de los agricultores y poniendo en riesgo la agrobiodiversidad, favoreciendo la mercantilización de semillas y la erosión genética.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.010
GPT teacher head0.230
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.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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