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Record W2921884461 · doi:10.11156/aibr.140103

Aproximación dialógica a los desastres tóxicos. El agente naranja en el valle A Luoi (Vietnam)

2019· article· es· W2921884461 on OpenAlexaboutno aff
Tak Uesugi

Bibliographic record

VenueAIBR Revista de Antropologia Iberoamericana · 2019
Typearticle
Languagees
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsnot available
FundersUniversity of CambridgeUniversity of Pennsylvania
KeywordsDialHumanitiesArtPhysicsAstronomy

Abstract

fetched live from OpenAlex

En la década de 2000, una consultora medioambiental canadiense llevó a cabo una investigación científica que impulsó una campaña de salud pública en el valle A Luoi de Vietnam central. En ella se informaba a los habitantes —por vez primera— sobre los riesgos y daños asociados al defoliante químico «agente naranja» y su dioxina tóxica y contaminante rociada durante la Segunda Guerra Indochina (1961-1975). En este artículo, en lugar de poner el foco en la identidad política formada a partir de esa información (aproximación «biosocial»), exploro cómo los habitantes experimentan el riesgo ante la sustancia tóxica en encuentros dialógicos con diferentes signos ambientales, así como con interlocutores reales e imaginarios.

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)0.002
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 score0.164
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.008
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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
Published2019
Admission routes1
Has abstractyes

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