Libel Cases and Public Debate – Some Reflections on whether Europe Should be Concerned about SLAPPs
Bibliographic record
Abstract
In recent years, Strategic Lawsuits Against Public Participation (SLAPPs) have become well‐recognized as challenging free speech and public participation in the USA, Canada and Australia. However, in Europe SLAPPs remain largely unrecognized with little consideration of their use and impact. This paper argues that SLAPPs are used in Europe and have been neglected for a number of reasons. In order to examine the European SLAPP situation, this paper focuses on libel law in England and Wales. It considers the debate on free speech that has flowed out of libel cases and concludes by reflecting on what advantages might flow from a refocusing of that debate that includes a recognition of SLAPPs.
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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 | 0.044 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.029 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".