Multiple Publication and Online Defamation – Recent Reforms in Ireland and the United Kingdom
Bibliographic record
Abstract
T he multiple-publication rule, allowing for a new cause of action each time a defamatory statement is published, has applied to non-internet publications for well over a century. Its application to online publications however, has raised particular difficulties. Despite the rule finding judicial favour in most common-law jurisdictions (the US being a noted exception) the legislature in Ireland has recently abandoned it and plans are in place in the United Kingdom to do likewise. On the other hand Australian and Canadian courts have rejected arguments to abandon the rule. This article discusses both the legal and policy related reasoning behind the recent legislative developments in Ireland and the United Kingdom and considers whether these jurisdictions have tipped the balance too far in favour of publishers on the internet.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".