The Impact of Corporate Governance and Audit Quality on the Cost of Private Loans*
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
ABSTRACT The objective of this paper is to examine whether banks discriminate between firms on the basis of their financial condition when assessing the credit default risk, and to what extent corporate governance and auditor quality mitigate such risks in the pricing of new bank loans. The results indicate that, depending on the probability of bankruptcy, banks rely on different monitoring devices. For firms with a low probability of bankruptcy, banks do not rely on the quality of corporate governance or the auditor's industry specialization. However, auditor tenure and a change in auditor affect the spread. For firms with a high probability of bankruptcy, the spread is adjusted for the quality of corporate governance and the auditor's specialization. These results are robust to alternative specifications and measures.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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