Bibliographic record
Abstract
Business and law are increasingly practiced on a transnational scale, and law firms are adopting new business structures in order to compete on this global playing field. Over the last decade, global law firms have merged into so-called “mega-brands” or “mega-firms”—that is, associations of national or regional law firms that join together under a single brand worldwide. For law firms, the most common mega-firm structure has been the Swiss verein, though the English “Company Limited by Guarantee” structure is growing in popularity as well, as is the similar “European Economic Interest Grouping.” All of these structures allow related entities to affiliate under a single brand, yet retain a separate legal identity. Law firms such as Baker & Mackenzie, Norton Rose Fulbright, and Dentons have all adopted the verein structure for their global practice. Each has separate legal entities practicing at a regional or national level (such as Dentons US LLP, or Norton Rose Fulbright Canada LLP), with the entities coming together under a single brand globally. As the mega-brand structure becomes more common, courts have struggled with how to treat imputed conflicts of interest. Is the verein (or similar entity) a single law firm, such that a client representation by one of the verein members will automatically prohibit other verein members from representing a client with conflicting interests? Or does the separate legal status of each of the verein members mean that Norton Rose Fulbright Australia could represent a client adverse to Norton Rose Fulbright US LLP—potentially even in the same proceeding? This article examines mega-firm conflicts from a client-protection perspective. It analyzes the policy goals underlying traditional rules on conflict imputation, including the need to protect client confidences and loyalty. It considers how conflicts of interest have been resolved in the mirror situation— that is, when law-firm clients are themselves global entities composed of related corporate entities—and analyzes how the conflict rules developed for related client entities could be adapted to fit global law firm vereins. The article ultimately argues that an overly broad imputation of conflicts carries real risk to clients and potential clients by limiting their ability to secure counsel of their choice. An approach focused more tightly on protecting the underlying values of confidentiality and loyalty can ensure client protection while still allowing clients to reap the benefit of innovation in law-firm business practices. Link to Jounral https://commons.stmarytx.edu/lmej/vol9/iss1/2/
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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".