The Rescue of the US Auto Industry, Module C: Restructuring Chrysler through Bankruptcy
Bibliographic record
Abstract
In late 2008, due to the confluence of the financial crisis and years of structural decline in the auto industry, Chrysler was nearing bankruptcy. The US Treasury provided Chrysler’s owner, Chrysler Holding, with a $4 billion bridge loan and Chrysler’s related finance company, Chrysler Financial, with a $1.5 billion financing program under the Troubled Assets Relief Program (TARP). The government-led restructuring through bankruptcy involved the commitment of roughly $5 billion in debtor-in-possession (DIP) loans from the US Treasury and the Canadian government, under which the US Treasury ultimately lent $1.89 billion, using TARP funds, and Canada lent about $1 billion, proportional to its share of the North American Free Trade Agreement (NAFTA) auto industry. It also involved concessions from stakeholders, corporate governance arrangements for the “New Chrysler,” and a merger with Italian automaker Fiat Automobiles S.p.A. Treasury financed the purchase by the New Chrysler of substantially all of the old Chrysler’s assets with a $7.14 billion loan. The bankruptcy case was controversial and nearly reached the US Supreme Court, but the restructuring ultimately rescued Chrysler. In the Chrysler rescue, Treasury lost about $2.93 billion on an investment of about $10.47 billion.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".