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Record W2003149431 · doi:10.7202/1017517ar

Eco-terrorists Facing Armageddon: The Defence of Necessity and Legal Normativity in the Context of Environmental Crisis

2013· article· en· W2003149431 on OpenAlexvenueaboutno aff
Hugo Tremblay

Bibliographic record

VenueMcGill Law Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsCivil disobedienceEnvironmental lawContext (archaeology)Political scienceNormativeValue (mathematics)Scope (computer science)InvocationEnvironmental degradationLawResilience (materials science)Law and economicsSociologyPoliticsEcologyGeography

Abstract

fetched live from OpenAlex

The invocation of necessity as a defence for acts of civil disobedience has raised questions about the rule of law and legal certainty. The rise of radical environmental activism in the context of climate change warrants an inquiry into the scope and limitations of the defence in Canada. This paper argues that the defence of necessity significantly increases legal flexibility in Canadian environmental law. To some extent, the defence may thus enhance the law’s resilience to socio-ecological changes. However, the defence could also render the law flexible to such an extent that positive norms might lose their prescriptive value in certain circumstances. In particular, as the link connecting human activity, climate change, and consequent damage to the environment becomes clearer, there is a greater likelihood of environmental activists successfully invoking necessity to defend illegal acts aimed at curbing environmental degradation. In other words, necessity may offer a defence against the enforcement of legal frameworks de facto authorizing catastrophic environmental destruction. The prescriptive value of those legal frameworks could be critically diminished, and the resilience of the law as a normative framework may be threatened.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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 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

Citations3
Published2013
Admission routes2
Has abstractyes

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