Bibliographic record
Abstract
In 2009, the banking industry continued to feel the fallout from the financial crisis that began in mid-2007. Some good news was revealed in recently available first-quarter data, however, which showed profitability rebounding and increases in asset-quality problems slowing down. Whether measured by profits or problems, Eleventh District banks were roughly \\"twice as good and half as bad\\" as their counterparts across the nation. Most likely, this reflects the fact that the economic downturn was less severe in the district than in other parts of the nation. ; Another noticeable difference emerges when comparing district banks' recent performance with an earlier period when the economy turned south and the industry suffered significant damage--the mid- to late 1980s. At that time, students of banking history may recall, a sharp decline in oil prices triggered a deep regional recession. Bank failures soared, and the financial landscape in Texas and other parts of the Southwest changed considerably. ; This raises the question of why the district's banking industry has been able to weather the current downturn--so far--with less damage than in the 1980s. The answer likely can be found in the changing nature of the district's economic environment since then.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".