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Record W4254497270 · doi:10.1017/cbo9781107110311.008

Conclusions

2014· book-chapter· en· W4254497270 on OpenAlexaff
Douglas Walton

Bibliographic record

VenueCambridge University Press eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Previous chapters have shown that formal dialogue models of argumentation, along with tools from AI like the Carneades Argumentation System and the abstract argumentation framework, apply very well to modeling burden of proof and presumption in the well-organized, rule-directed framework of a legal trial. The objection posed in Chapter 1 was that the legal concepts of burden of proof and presumption have been illicitly transferred from the legal setting to public policy discussions and other arenas where the argumentation is not structured in the same way it is in a legal setting. Chapter 8 takes up this challenge by arguing that the formal dialectical framework of Chapter 4 can be usefully applied to modeling burden of proof and presumption in these other settings. This argument, of course, does not claim the burden of proof and presumption work in every respect in the same way they work in other settings. It only means that the methods used to reason about evidence in the common law system presents an outline of reasonable but defeasible argumentation that has some important features, represented in the dialogue models of burden of proof and presumptions presented in the previous chapters of this book, and these features can be adapted to and applied in a helpful manner to other settings of argumentation outside law. Of course there are many different settings in which argumentation is used as a means of proving a hypothesis, settling a conflict of opinions based on evidence brought forward or arriving at a rational decision on how to make a choice in a deliberation on what to do in a situation requiring such a choice, as indicated in Chapter 7. For the purposes of this book, however, there are two main settings that seem to be of main interest in relation to the issue of transferability of the legal notions of presumption and burden of proof. One is the kind of organized disputation of the kind represented in a forensic debate, or to cite a specific example, a presidential debate that has a moderator and is televised to a large audience.

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)0.003
metaresearch head score (Gemma)0.009
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: Other · Consensus signal: Other
Teacher disagreement score0.238
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0070.005
Open science0.0020.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.2380.088

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 head0.053
GPT teacher head0.270
Teacher spread0.217 · 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
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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