takes responsibility for any errors. Recent De Novo Bank Failures:
Bibliographic record
Abstract
Abstract: Since 2007, the number of bank failures has soared. A large percentage of the failures involved young banks formed during the mid-1990s amid the wave of new bank charters granted by federal and state banking supervisors. De novo bank supervisory choices could influence their failure risk if charter-related disadvantages exist and are large, or if the ability of banks to choose among multiple supervisors results in more lenient supervision and facilitates greater bank risktaking. The sample consists of 1,015 de novo banks opened from the third quarter of 1996 through the first quarter 2003. The study uses a competing risk hazard model to identify the significant determinants of failure or voluntary merger for the sample banks through the end of second quarter of 2010. More than 7 percent of the sample banks changed their initial primary federal supervisor during the observation period. More than half of these supervisory changes also involved a charter conversion, with shifts away from supervision by the Office of the Comptroller of the Currency (OCC) accounting for most of this activity. The empirical analysis found no evidence that banks starting with and maintaining a national charter were more likely to fail than state chartered banks. The evidence does show that some,
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 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 | 0.025 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.205 | 0.256 |
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".