Book Review: Global Forces of Corporate Change and European Path Dependencies, A Review of After Enron (McCahery/Armour eds.)
Bibliographic record
Abstract
The book under review, After Enron, edited by John Armour and Joseph McCahery, and published by Hart in 2006, presents an excellent and timely collection of observations of the Enron debacle, provided by some of the most astute and informed scholars, and masterfully integrated by two of the finest academics in this field. The editors, Dr John Armour, originally of the Faculty of Law at the University of Cambridge and Member of the Cambridge Centre for Business Research, since 1 July 2007 the Lovells Professor of Law and Finance, and Professor Joseph McCahery, formerly at the University of Tilburg, now of the University of Amsterdam, have succeeded in collecting, conceptualizing and organizing a most comprehensive and intriguing collection of excellent writings on Enron and its aftermath. Their book can aptly serve for a first-blush as for a more in-depth analysis of the problems, whether in research or in teaching of company law courses. Yet, beyond this achievement, the editors are also importantly contributing to a debate, which has for some time now emphasized the need to take a deliberately comparative viewpoint when analyzing the trajectories of corporate law development around the world
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".