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Record W4281645307 · doi:10.18280/ijsdp.170320

Punishing Environmental Offenses Without Guilty Mind: Regulatory Framework and Judicial Responses

2022· article· en· W4281645307 on OpenAlexvenueno aff
Machrus Ali, Muhammad Arif Setiawan, Wawan Sanjaya

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsnot available
FundersUniversitas Islam Indonesia
KeywordsCulpabilityElement (criminal law)LawLegislationHarmLiabilityLegal liabilityCriminal lawScope (computer science)Strict liabilityPolitical scienceMens reaBusinessPsychologyCriminologyComputer science

Abstract

fetched live from OpenAlex

This paper explores the legal considerations and the scope of application of criminal liability without guilty mind in environmental offenses in Indonesia. Under the existing Environmental Law, liability without fault has been applicable exclusively in civil cases. This paper combines literature and induction research methods. The first method dives into the legal provision in Environmental Law containing the formulation of the offenses, while the induction method refers to the analysis on judicial decisions in the application of liability without fault. The findings of the study show that most of the prohibited offenses in environmental legislation deal with the malum prohibitum crime tied to the violation of a permit. The mental element is not explicitly stated in these offenses. Hence, the culpability of the defendant is presumed to be displayed in the evidence of the prohibited conduct. Waste or emissions discharged into the environmental media without authorisation is prohibited and pertaining the potential to harm the environment. These offenses are included as formal offenses by removing the element of culpability in the structure of the offense. It is also sufficient for the court to rule that the defendants have committed the prohibited conduct as the basis for imposing criminal sanction.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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 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
Published2022
Admission routes1
Has abstractyes

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