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 revolving credit line is one of the most common forms of commercial bank loan. Fixing the interest rate and the maximum loan amount but not the utilization pattern introduces several types of uncertainty into the contract. In practice, in addition to the interest on the drawn amount, a variety of different fees and charges may be imposed, although generally not all at once. This leads to interesting optimal behavior for the borrower in the face of stochastic fluctuation in market interest rates and borrower credit quality. For example, the borrower can raise funds in the open market if the interest rate is lower there but has the option to draw against the line at the original rate if its creditworthiness weakens. Jones and Wu present a model incorporating these special features and explore how they affect optimal loan terms and borrower behavior. Interesting results include the fact that because of the borrower’s option to draw on the credit line when its creditworthiness weakens, the lender cannot make money on the deal without incorporating extra fees on top of the interest on the borrowed principal. <bold>TOPICS:</bold> <ext-link>Real assets/alternative investments/private equity</ext-link>, <ext-link>quantitative methods</ext-link>
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.
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 | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it