Bibliographic record
Abstract
Borrowers can raise funds from a competitive banking sector that shares information and from opaque hidden lenders. Hidden lenders allow borrowers to conceal poor results, and thereby affect contracts in the banking sector. In equilibrium, borrowers obtain funds from both sectors simultaneously. The lack of transparency generates cross-subsidies between different borrowers who are observationally equivalent to banks and face the same interest rate. As the cost of hidden borrowing falls, an increasing number of borrowers face identical terms; for sufficiently low costs, all borrowers who take loans (which may include inefficient borrowers) use the same bank debt contract. (JEL G21, D82, D86) Firms and households have access to various sources of borrowing. The differences in seniority, covenants, and interest rates may induce an ap-parent “pecking order ” among loans. However, loans also differ in the extent of their transparency to other lenders. Whereas some lenders perfectly share information—through a public credit registry, for ex-ample—other lenders may not engage in such information sharing.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".