Financial Assurance versus Liability as Solutions to the Judgment-Proof Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A firm is said to be judgment-proof if it can cause an accident and then skirt its environmental liabilities by pleading bankruptcy. Judgment-proofness is a public-policy problem because it saddles society with uncompensated liability and stifles the firm’s incentive to undertake due care. We employ a simple lending model that incorporates moral hazard to compare three instruments designed to remedy the judgment-proof problem; namely, environmental bonds, mandatory liability insurance and liability. Incentives are unambiguously stronger under liability than they are under either bonds or mandatory insurance. Credit is more rationed and the span of potentially damaging projects is lower under bonds and mandatory insurance than they are under liability. The relative impact of these instruments on social welfare ambiguously depends on entrepreneurial wealth.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it