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Record W2113893373 · doi:10.1007/s12152-010-9078-0

“The Neuroscience of Responsibility”—Workshop Report

2010· article· en· W2113893373 on OpenAlexaboutno aff
Nicole A. Vincent, Pim Haselager, Gert‐Jan C. Lokhorst

Bibliographic record

VenueNeuroethics · 2010
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
Fundersnot available
KeywordsRelevance (law)Engineering ethicsPsychologyNeuropsychologyLibrary scienceNeurolawPolitical scienceSocial neuroscienceNeuroscienceEngineeringLawCognition

Abstract

fetched live from OpenAlex

This is a report on the 3-day workshop "The Neuroscience of Responsibility" that was held in the Philosophy Department at Delft University of Technology in The Netherlands during February 11th-13th, 2010. The workshop had 25 participants from The Netherlands, Germany, Italy, UK, USA, Canada and Australia, with expertise in philosophy, neuroscience, psychology, psychiatry and law. Its aim was to identify current trends in neurolaw research related specifically to the topic of responsibility, and to foster international collaborative research on this topic. The workshop agenda was constructed by the participants at the start of each day by surveying the topics of greatest interest and relevance to participants. In what follows, we summarize (1) the questions which participants identified as most important for future research in this field, (2) the most prominent themes that emerged from the discussions, and (3) the two main international collaborative research project plans that came out of this meeting.

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)0.015
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0090.005
Open science0.0030.012
Research integrity0.0080.013
Insufficient payload (model declined to judge)0.0140.007

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 head0.169
GPT teacher head0.366
Teacher spread0.197 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2010
Admission routes1
Has abstractyes

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