Book Review: Debt's Dominion: A History of Bankruptcy Law in America, by David A. Skeel Jr.
Bibliographic record
Abstract
Debt's Dominion is a mix of political intrigue and historical drama in the evolution of U.S. bankruptcy law.The number of bankruptcies has skyrocketed in the United States, with more than one million individuals filing for bankruptcy annually.The U.S. bankruptcy regime, like those of most developed market economies, provides corporations with a liquidation option and a reorganization option.Similarly, personal bankruptcy offers two avenues of discharge: straight liquidation, in which the individual debtor relinquishes all assets except specified exemptions; and a rehabilitation option, where the debtor retains all assets and a portion of income is directed towards partial repayment of the debt over a defined period.Skeel provides a well-researched and comprehensive description of the historical development of the U.S. system.Skeel attempts a "fully theorized explanation" of the current bankruptcy regime.3 He grounds his account in public choice theory, and the political and institutional devices used by special interest groups to advance and protect their normative vision of bankruptcy law.At the heart of his account is the public choice notion that concentrated interest groups frequently benefit at the expense of widely scattered groups, even if the
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".