Retail cascading in Germany a model for a revision of the PD?
Bibliographic record
Abstract
The EU Prospectus Directive1 (the ‘PD’), as implemented in several EEA Member States, including the United Kingdom and the Regulation accompanying the PD2 (the ‘Regulation’) render difficult or even inhibit public offers of debt securities to retail investors. The article by Lachlan Burn and Boyan Wells in the preceding issue of this Journal3 discusses the existing problems when a debt offering is made through a retail cascade.4 Principally, the problems in these Member States are twofold. First, the possible need to produce a PD compliant prospectus at each level of the distribution chain, unless an exemption applies. Second, the disclosure requirements in Annex V.5 of the Regulation. They are rightly held to be unsuitable and inappropriate for retail debt offerings.5 In addition, there is concern how an investor might know that a specific offeror in the cascade is or is not acting in association with the issuer, as this would determine whether the particular offer at one of the levels of the cascade is one to which the prospectus relates or is a separate unrelated offer.6
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".