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Record W6894049831 · doi:10.5281/zenodo.7046788

5 PONTOS CRÍTICOS PARA DISCUTIR LAWS

2022· article· pt· W6894049831 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languagept
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsOrder (exchange)Doctrine

Abstract

fetched live from OpenAlex

Ao nos depararmos com imagens provenientes das guerras na era da pós-modernidade, temos a impressão de viver em um vero e proprio anacronismo histórico: armamentos do futuro contrastam com um conjunto de valores do passado, como os nacionalismos, os expansionismos e o revanchismo. Nessa espécie de erro cronológico, os LAWS (sigla em inglês para Sistemas de Armas Letais Autônomas) emergem como a nova forma de fazer guerra, dando impulso à era da portabilidade e mobilidade bélica em níveis jamais vistos antes. Esta tecnologia, que barateia e transforma a forma de guerrear, coloca em curso a tendência global de uma nova “corrida” ou, talvez, um novo "sobrevoo armamentista” entre Estados. Não obstante os dilemas morais e éticos e às transgressões aos Direitos Humanos e Individuais representados pelos uso dos LAWS pelos Estados, presenciamos uma conjuntura internacional na qual a elaboração de uma proposta de Convenção da ONU de proibição e regulação tipo hard law parece estar longe de atingir um consenso. Neste Policy Brief, pontuamos algumas questões que permeiam o debate entre os especialistas que analisam criticamente os LAWS e propomos uma comparação com a regulação de outra arma, sua predecessora analógica: as Minas Antipessoais Terrestres, que foram reguladas através do Tratado de Ottawa (1997). Neste sentido, nosso intuito é investigar pontos comparativos durante o processo de construção consensual que possibilitou a aprovação de tal legislação, verificar os princípios e os precedentes levantados pelos diversos atores sociais com a finalidade de embasar paralelos que indiquem uma orientação visando a construção de uma proposta.

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)0.026
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0120.080
Scholarly communication0.0150.019
Open science0.0060.008
Research integrity0.0180.033
Insufficient payload (model declined to judge)0.0090.003

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 head0.111
GPT teacher head0.274
Teacher spread0.163 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2022
Admission routes1
Has abstractyes

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