MétaCan
Menu
Back to cohort
Record W3014497230 · doi:10.1017/9781316443712.007

Legal Issues Involving Memory

2019· book-chapter· en· W3014497230 on OpenAlexaff
Walter Glannon

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmnesiaPsychologyObligationCognitionAction (physics)Dissociation (chemistry)Social psychologyCognitive psychologyPolitical scienceLawNeuroscience

Abstract

fetched live from OpenAlex

This chapter argues that one can have some memory loss over time but remain the same person and be held responsible for one’s earlier actions. Amnesia following an action does not entail that the agent had no cognitive or volitional control when he acted. Amnesia as such is not a mitigating or excusing condition. But an individual who had undergone a substantial identity change from extensive memory loss could not be held responsible or punished because he would have become a different person. The chapter also considers dissociative disorders such as somnambulism. The main question regarding these states is whether they impair a person’s capacity to form and translate an intention into a criminal act. Dissociation comes in degrees. A person in a dissociative state may have enough behavior control to be at least partly responsible for her actions. In addition, the chapter examines memory loss in omissions and whether it can be a mitigating factor in cases involving negligence causing death. The chapter also argues that a victim of an assault does not have an obligation to retain a memory of it to testify against the perpetrator. Her cognitive liberty gives her the right to erase the memory.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0210.004

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 head0.262
Teacher spread0.196 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicNeuroethics, Human Enhancement, Biomedical InnovationsFrench-language works237,207