Split Ratings and Differences in Corporate Credit Rating Policy between Moody's and Standard & Poor's
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 This paper investigates split credit ratings awarded by Moody's and Standard & Poor's (S&P) to U.S. corporations. Bivariate probit model estimates, analyzing 5,238 firm‐year observations from dual‐rated S&P 500/400/600 index‐constituent corporations, indicate firm‐specific financial and governance characteristics predict split ratings. Large, profitable companies with enhanced interest coverage, a greater percentage of independent directors, and more institutional investment are less likely to receive splits. Moody's appears more conservative in its evaluations, assigning lower ratings to smaller, less profitable companies with low interest coverage. Moody's also associates external, independent constraints on managerial autonomy with a higher corporate credit standing relative to S&P.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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