Bibliographic record
Abstract
On December 22, 2008, Bank of America (BofA) chairman and CEO Ken Lewis convened a special board of directors meeting to review his company's pending acquisition of investment bank Merrill Lynch. Negotiations for the acquisition had begun a few months earlier, during the disastrous week in September in which Lehman Brothers declared bankruptcy. Initially both Merrill and BofA viewed their agreement favorably, but in the intervening months, as Merrill's anticipated losses ballooned and the government stepped in with such programs as the TARP, BofA found itself tied to a financial anchor with a hard-line from the government that prevented BofA from abandoning ship. This case provides background on the financial crisis and the chain of events between September and December of 2008 in which Merrill, BofA, and the government attempted to negotiate the acquisition. This case focuses class discussion on several decisions--whether BofA should have initially agreed to buy Merrill Lynch, whether it should have accepted capital contributions from the Treasury, and how it should have responded to the deterioration in Merrill Lynch's position in the first quarter.Learning Objective: To understand how banks responded to the financial crisis, and evaluate the various forms of federal assistance offered to banks during the crisis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".