The Going Private Phenomenon: Causes and Implications
Bibliographic record
Abstract
of Chicago Law Review.This topic could not be timelier, as the past several years have seen an unprecedented number of public companies being taken private through leveraged buyouts.The list includes: BellCanada ($35 billion),' Alltel ($28 billion), 2 Sun-Guard Data Systems ($11 billion), 3 and Toys "R" Us ($6 billion).'These deals are part of a growing trend large enough to be deemed a phenomenon worthy of study by lawyers, economists, and other serious students of American business.Although the credit crunch and financial crisis of 2008 has dampened the enthusiasm for private-equity deals, in the long haul the trend is likely to continue, as private-equity firms hold in reserve hundreds of billions of dollars in capital waiting for deployment, and the model has demonstrated significant efficiencies.The privatization of large swaths of the economy raises a number of significant questions, including: whether public shareholders are being adequately compensated in these transactions (and what the legal system can do about it if they are not); what the long-term effect of these transactions is on constituent groups-shareholders, suppliers, cred-1 Rob Gillies, Bell Canada Agrees to Record $35-billion Leveraged Buyout, LA Tnies C2 (July 2,2008). 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".