Bibliographic record
Abstract
Finance is partly international since financial assets have such high liquidity. Many factors, including national financial institutions, financial markets, financial products, and others, contributed to the financial crisis. People's expectations for the future of the economy are pessimistic, the currency of the region has produced a sizeable budget, the scale and aggregate of the economy have been significantly reduced, domestic economic growth has been severely harmed, and many businesses have closed, driving up the unemployment rate. Moreover, there is some social unrest and political unrest along with a general decline in the social economy. On August 9, 2007, the global financial crisis, also known as the credit crisis and lasting from 2007 to 2009, began. After the early subprime home credit crisis arose, investors began to lose trust in the value of mortgage instruments, which caused a liquidity crisis. Despite continuously pumping large amounts of money into the financial system, the central banks of multiple different countries were unable to halt the financial crisis from beginning. The financial crisis had been under control up until September 9, 2008, but it had begun to spin out of control, leading to the failure of numerous significant financial institutions or government takeover.
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 | 0.001 | 0.003 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".