Bibliographic record
Abstract
This article explains how developers and users of large language models (LMs) may be treated by English and Canadian libel law. LMs could be economically significant, and the liability environment they exist in will affect where they are developed, who accumulates wealth from their development, and who bears the burdens of any negative consequences of their development. Understanding the existing liability environment allows both developers and policy makers to make informed decisions—about which jurisdiction to offer services and what to prioritise, for the former, and about whether the existing law serves desired policy ends, for the latter. LMs also raise challenging legal issues because they undermine common-sense assumptions that are baked into existing legal doctrines. Although the discussion may have broader implications for tort law generally, this article focuses on the doctrine and theory of libel. Legal problems lie lurking in those doctrinal weeds and are helpfully revealed by the contrast between Canadian and English law. Minor jurisprudential differences in decisions from yesteryear may have significant consequences if they are followed when text is generated by LMs rather than by people.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".