Bibliographic record
Abstract
Asset management, a distinctive sector within the financial services industry, centers on an agency relationship between a client and an individual manager or firm appointed to manage the client's investment portfolio. Additionally, in many jurisdictions asset managers are subject to a technically complex set of regulatory requirements, which differ across jurisdictions. This book is the only comparative analysis of the law of asset manager liability in the major European jurisdictions, the United States, and Canada, with chapters written by specialists from the relevant jurisdictions plus a comprehensive chapter covering the relevant European law, in particular the MiFID directive. The book's coverage is limited to relationships that pertain to individual portfolios of securities, as opposed to collective investment schemes such as mutual funds and UCITs. A central focus is how regulation interacts with civil liability, whether based on breaches of duties imposed by general law (such as breach of fiduciary duty and duties of care) or on breaches of duties imposed by regulation itself. The Introduction, co-authored by the book's co-editors, situates the country-by-country materials within the broader context of questions about regulatory design and effectiveness. These include whether regulation and liability should be understood as substitutes for each other or as necessary complements; differences in the style of regulation; the role of industry-based self-regulation; and the impact of mandated disclosure of information by asset managers.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.020 |
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".