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Record W2915475616 · doi:10.60082/2817-5069.3388

“O! they have lived long on the alms-basket of words”: Enhancing Efficacy and Reducing Cost by Limiting the Role of Law and Lawyers in Defamation Disputes

2019· article· en· W2915475616 on OpenAlexvenueno aff
Andrew Scott

Bibliographic record

VenueOsgoode Hall law journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFreedom of Expression and Defamation
Canadian institutionsnot available
Fundersnot available
KeywordsRedressReputationCounterfactual thinkingFree speechLawLaw and economicsMeaning (existential)LimitingSociologyPolitical scienceEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

To triangulate the individual and social interests in reputation and free speech, the common law has generated an unwieldy corpus of technical rules and counterfactual assumptions. This complexity entails enormous cost and opportunities for game-playing by astute, well-resourced litigants. Neither reputation nor free speech is well-served by reform initiatives that focus mainly on amending the substantive law. This paper offers a critical assessment of a proposal that might better address complexity and cost. This comprises the inextricable combination of two initiatives: repeal of the ‘single meaning rule’ which promises to simplify the court’s task, but instead generates complexity in defiance of common sense; and a bar to claims should a publisher correct unintended meanings promptly and prominently. This combination would eliminate the ‘semantic’ dimension of claims; deliver adequate redress for many publication harms; see only intractable disputes reach the court; and leave defamation law more comprehensible to the wider public.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.273
Teacher spread0.256 · 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 teacher head, 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

Citations0
Published2019
Admission routes1
Has abstractyes

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