A U.S. Perspective on the Contextual Terrain of Political Economy in Insolvency Reform
Bibliographic record
Abstract
The legislative history of the 2005 revisions to the U.S. Bankruptcy Code has been well documented. Yet these revisions created a puzzle for political economists. If, as other scholars (mostly rightly) contend, American debtors enjoy lobbying power that would make their foreign counterparts blush, where did they go wrong in 2005? Indeed, a comparison to the Canadian experience, where insolvency laws were also recently amended, only sharpens this puzzle. In Canada, commercial banks wallow in well-concentrated power; yet there the outcome was much more debtor-friendly than in the United States. Traditional political economy accounts would have predicted a harsher Canadian law and a softer U.S. one, but closer to the opposite was the case. This book chapter probes this unexpected outcome. Its (concededly rudimentary) political economy analysis suggests that the divergence lay in the different "terrains" of the insolvency lobbying landscape. Creditors in the United States reframed the initial debate, which enabled them to discredit and distract potential adversaries, and which in turn helped them overcome the apparently superior bargaining endowment of debtors (or, more precisely, the debtors' proxies in the bench, bar, and academy). The Canadian debtors escaped this fate. As such, the traditional accounts still generally hold; it was the political economy landscape - the "lobbysphere" - that was different.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".