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Record W2080170904 · doi:10.7202/1028144ar

Responsibility and Intervening Acts: What “Maybin” an Overbroad Approach to Causation

2015· article· en· W2080170904 on OpenAlexaffvenueabout
Terry Skolnik

Bibliographic record

VenueRevue générale de droit · 2015
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsCausationBlameSupreme courtAction (physics)Mens reaLawCriminal lawPsychologyWrongful deathPolitical scienceSpeculationCriminologyLaw and economicsSocial psychologySociologyBusinessDamages

Abstract

fetched live from OpenAlex

Oftentimes, a criminal action resulting in the victim’s death is clearly attributable to the accused. In many cases, we can easily say that the accused “caused” the victim’s death. Causation, however, becomes particularly complicated when some type of intervening act occurs between the accused’s initial conduct and the victim’s death, creating speculation about whether it is fair to morally blame the accused for the ensuing result. The Supreme Court of Canada’s relatively recent decision R v Maybin marks a significant attempt to clarify notions related to causation in the criminal law. Although the Court refused to alter conventional principles related to the law of causation, or create a new test to verify when it has been established, it provided two analytical tools which can be used in order to see when it is fair to morally blame the accused for the victim’s death despite an intervening act’s occurrence. As will be seen, although these analytical tools of “reasonable foreseeability” and “independent acts” serve to simplify the law of causation, there are important problems with how each tool has been conceptualized. This article highlights these important shortfalls, and ultimately, questions to what extent these developments in the law of causation affect current conceptions of mens rea .

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.066
GPT teacher head0.284
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2015
Admission routes3
Has abstractyes

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