“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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".