Surviving the Global Recession and the Demand for Auto Industry in The U.S. – A Case for Ford Motor Company
Bibliographic record
Abstract
The world economy has been severely affected by the global recession which started from the second quarter of 2007 triggered by the financial crisis. The auto industry in the U.S. faced the most severe difficulties which threatened its survival after the recession. In the U.S. especially the “Big 3” the General Motors, Ford, and Chrysler struggled to stay in the business. This paper analyzes the impact of recent downturn on U.S. auto industry in general, and the demand of Ford vehicles in particular. The study also discusses the past and present performance of Ford Motor Company in the light of changing economic conditions at home and abroad. The empirical study uses twenty years sales data to estimate a time-series demand model for Ford vehicles.The study found that the demand for automobiles in the U.S is positively related to non-farm employment and single family housing start and negatively related to gas price and vehicle price.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".