The Reform of Electronic Consumer Contracts in Europe: Towards An Effective Legal Framework?
Bibliographic record
Abstract
Christine Riefa is a lecturer in Consumer Law and Intellectual Property Law at Brunel University in the UK. She is an elected board member of the International Association of Consumer Law and an academic correspondent to the Euro-American Chair for the legal protection of consumers (University of Cantabria, Spain). In 2009-2010, Dr Riefa is a Fulbright EU Scholar-in-Residence at Cleveland-Marshall College of Law, Ohio USA. A first version of this article was presented at the Summer School in Consumer Law, organised by the GREDICC (Groupe de recherche en droit international et comparé de la consummation), UQAM, Montréal, 29th June – 4th July 2009.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".