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Record W2131829871 · doi:10.1111/jlme.12096

Another Look at the Legal and Ethical Consequences of Pharmacological Memory Dampening: The Case of Sexual Assault

2013· article· en· W2131829871 on OpenAlexafffund
Jennifer A. Chandler, Alexandra Mogyoros, Tristana Martin Rubio, Éric Racine

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsMontreal Clinical Research InstituteUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsPromulgationWitnessPsychologySexual assaultObligationEthical issuesCriminologyMedicineSocial psychologyPsychotherapistPolitical scienceLawPoison controlHuman factors and ergonomicsEngineering ethicsMedical emergency

Abstract

fetched live from OpenAlex

Research on the use of propranolol as a pharmacological memory dampening treatment for post-traumatic stress disorder is continuing and justifies a second look at the legal and ethical issues raised in the past. We summarize the general ethical and legal issues raised in the literature so far, and we select two for in-depth reconsideration. We address the concern that a traumatized witness may be less effective in a prosecution emerging from the traumatic event after memory dampening treatment. We analyze this issue in relation to sexual assault, where the suggestion that corroborating evidence may remedy any memory defects is less likely to be helpful. We also consider the clinical ethical question about a physician's obligation to discuss potential legal consequences of memory dampening treatment. We conclude that this latter question reflects a general problem related to novel medical treatments where the broader socio-legal consequences may be poorly understood, and suggest that issues of this sort could usefully be addressed through the promulgation of practice guidelines.

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)0.009
metaresearch head score (Gemma)0.031
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: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.017
Scholarly communication0.0060.008
Open science0.0020.004
Research integrity0.0230.017
Insufficient payload (model declined to judge)0.0040.000

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 head0.133
GPT teacher head0.421
Teacher spread0.288 · 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
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

Citations6
Published2013
Admission routes2
Has abstractyes

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