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Record W2740952829 · doi:10.1017/cjlj.2017.15

Unreliable Narration in Law and Fiction

2017· article· en· W2740952829 on OpenAlexaboutno aff
Daniel Del Gobbo

Bibliographic record

VenueCanadian Journal of Law & Jurisprudence · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAgathaInterpretation (philosophy)Meaning (existential)Law and literatureLawSociologyLiteraturePsychologyHistoryPolitical scienceLinguisticsEpistemologyArtPhilosophy

Abstract

fetched live from OpenAlex

This article revisits long-standing debates about objective interpretation in the common law system by focusing on a crime novel by Agatha Christie and judicial opinion by the Ontario High Court. Conventions of the crime fiction and judicial opinion genres inform readers’ assumption that the two texts are objectively interpretable. This article challenges this assumption by demonstrating that unreliable narration is often, if not always, a feature of written communication. Judges, like crime fiction writers, are storytellers. While these authors might intend for their stories to be read in certain ways, the potential for interpretive disconnect between unreliable narrators and readers means there can be no essential quality that marks a literary or legal text’s meaning as objective. Taken to heart, this demands that judges try to narrate their decisions more reliably so that readers are able to interpret the texts correctly when it matters most.

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)0.007
metaresearch head score (Gemma)0.047
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0090.035
Scholarly communication0.0110.013
Open science0.0010.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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