Is 'Truthtelling' Online Reasonable? Restoring Context to Cyber Defamation Analysis
Bibliographic record
Abstract
The author thanks the Law Commission of Ontario and acknowledges the research initially carried out as a contract researcher for the LCO. The views expressed this subsequent work are not necessarily those of the LCO. This paper proposes to re-orient cyber defamation analysis towards a Civilian approach, whose hallmark flexibility and adaptability lends itself particularly well to the digital age. Indeed, harnessing the ordinary rules of negligence, and – in principle – foregoing defences , the Civilian construction is chiefly interested in the contextual reasonableness of the impugned expression (rather than in its truth or falsity strictly speaking), in contradistinction to its somewhat categorical Common Law counterpart. It is therefore recommended that defamation law evolve towards a ‘negligence standard’ in common law parlance. Plainly put, this would require the plaintiff to make a showing of the contextual unreasonableness of impugned speech, an analysis which subsumes truthfulness and obviates the need for defences, this comporting with constitutional imperatives. Moreover and compounding the importance of revisiting the matter, “in a world where boundaries are porous and shifting” – and data is global, a cyber-publication in one jurisdiction may be read and reposted anywhere in the world, thereby potentially causing reputational harm transcending traditional or national parameters. Therefore, enforcing rights flowing from conduct originating outside of Canada increasingly preoccupies our courts who are gradually fearful of losing the ability to enforce local norms and policy or rectify domestically felt harm originating elsewhere. This preoccupation with 'judicial helplessness' in Internet cases is evidenced by the notably liberalized jurisdiction test in Goldhar and Black inter alia and by two landmark cyber jurisdiction oriented cases handed down by the Supreme Court of Canada in 2017 alone. It is therefore essential to at least summarily address the jurisdiction question – if we are to have a true contextual understanding of cyber defamation as recommended herein.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".