An analysis of three distinct approaches to using defamation to protect corporate reputation from Australia, England and Wales, and Canada
Bibliographic record
Abstract
Abstract The use of defamation law to protect corporate reputation is controversial. Australia, Canada and England and Wales have been at the centre of this debate, as although their defamation laws share many common characteristics, they adopt distinct approaches to allowing companies to sue in defamation. Consequently, in all three jurisdictions defamation law remains a cause of action that is relied upon by companies to protect their reputations. The primary concern of this paper is the efficacy of these approaches,1 particularly in light of the reforms made to Australia's defamation laws, adopted in 2020, that further restrict the right of corporations to sue in defamation. Ultimately, it argues that the Australian and English and Welsh approaches disproportionately disadvantage companies, particularly small ones, whereas the Canadian approach overprotects corporate reputation. It concludes by offering an alternative way forward that, although not perfect, provides a better balance between the interests.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".