The new directive on an EU-wide representative action and third-party litigation funding: An opportunity for European consumers?
Bibliographic record
Abstract
After years of compensatory collective redress being left to a sort of regulatory competition among Member States, Directive 1828/2020 finally introduced an EU wide representative action scheme, aimed at strengthening the position of European consumers vis-à-vis new market dynamics such as globalisation and digitalisation. The new system, which shall run in parallel with national tools, introduces some innovations such as a cross-border action mechanism, the possibility of adopting an opt-out model and a specific regulation of third-party litigation funding in the context of collective redress. This aspect, addressed already in the 2013 Recommendation, is of particular interest, because third party funding represents a particularly powerful complement to collective redress in easing citizens' access to justice. However, the provisions introduced with Directive 1828/2020 leave some issues open. In particular, the Court's role in managing the funding agreement, with special reference to the funder's fee, and the effect of the funding agreement in case an opt-out adhesion mechanism is adopted are of paramount importance and still need to be addressed interpretatively. In this task, the comparative method will be particularly helpful in analysing the solution which Countries more familiar with third party funding, like Australia, Canada or the United States have introduced or discussed.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.027 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".