Mitigating Judgment Proofness: Information Acquisition vs. Extended Liability
Bibliographic record
Abstract
Abstract We present a simple lending model of judgment proof borrowers with private information and heterogeneous wealth, where large and small lenders coexist. Lenders subject would-be borrowers to pre-lending screening, which is not observable and cannot therefore be committed to ex ante. We document market segmentation and credit rationing: borrowers with limited wealth, who face a less severe moral hazard, are funded by large lenders; wealthy borrowers turn to small lenders who face a higher cost of capital; borrowers with abundant wealth do not get any financing. With advances in the screening technology, large lenders (small lenders) contract (expand) their loan portfolio. In contrast, as a result of an increase in the liability of lenders, large lenders (small lenders) increase (decrease) the number of loans that they make. The qualitative impact on social welfare of an increase in lender liability or advances in information technology is ambiguously tied to the quality of the borrower pool.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".