Gregory E. Sierra Banking Supervision and Regulation
Bibliographic record
Abstract
The views expressed in this paper are those of the authors, not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve System. Despite the consolidation of the banking industry in recent years, community banks continue to be a relevant portion of the banking industry. We identify community banks as those with assets less than $1 billion. 1 As of the fourth quarter of 2001, 85 percent of all banks had total assets less than $1 billion. Community banks are an important source of credit for small businesses, as they make a disproportionate share of small business loans. 2 While community banks accounted for about 15 percent of banking assets in the second quarter of 2001, they held about 40 percent of the number of business loans outstanding of less than $1 million. Furthermore, there is evidence that the failure of community banks can have adverse effects on local economic activity. 3 The condition of community banks is especially relevant for an assessment of the risk of loss by the deposit insurance fund, since the failure rates and FDIC loss rates on bank failures are inversely related to bank size (Shibut, 2001). We examine the condition of commercial banks in the United States, with
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".