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TRATAMENTO DE CRIMES AMBIENTAIS PELO TRIBUNAL PENAL INTERNACIONAL

2017· article· pt· W2982106757 on OpenAlexaff
Alexandre da Costa Pereira, Thiago Oliveira Moreira

Bibliographic record

VenueRevista da Escola Superior de Guerra · 2017
Typearticle
Languagept
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsTribunalHumanitiesCrimes against humanityPolitical scienceAmnestyPhilosophyWar crimeLawInternational lawHuman rights

Abstract

fetched live from OpenAlex

O presente trabalho trata da possibilidade de ampliação do escopo do Tribunal Penal Internacional no sentido de permitir que crimes ambientais sejam equiparados a crimes contra a humanidade, conforme analogia com o crime de genocídio, tipificado no Tratado de Roma. Na argumentação são abordadas questões referentes ao tema dos crimes contra a humanidade, a tipificação de crimes segundo o escopo do Tratado de Roma e o tema da contextualização da analogia entre crimes de guerra e crimes ambientais. Também é abordada no trabalho a temática da intenção de crime contra a humanidade em crimes ambientais, bem como sobre a nova figura penal do “Ecocídio” proposta por alguns autores que estudam o tema em tela. Comenta-se, como considerações finais, que, para o tratamento pleno dos crimes contra o meio ambiente no Estatuto de Roma, se faz necessário, diante da exigência de estrita tipificação das figuras delituosas que vigora, no campo penal, a inclusão do delito na jurisdição do Tribunal, juntamente com o tipo penal dos crimes de agressão, conforme previsto por Levandovski (2002), visando possibilitar a aplica- ção da pena, com a devida caracterização do tipo no Estatuto por ocasião da eventual reforma do diploma.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.059
GPT teacher head0.332
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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