Bibliographic record
Abstract
For U.S. companies with large trade and investment exposures to Western Europe, the year 2000 was a very difficult time. During that year, the euro fell in value from just under US$1.00 to approximately $0.80. U.S.-based firms such as Compaq, IBM, Intel, Polaroid, Microsoft, Baxter International, Heinz, Caterpillar, Dow Chemical, Dupont, and TRW all suffered as a result. Why? Their euro sales were worth less in dollar terms, and dollar terms mattered. One Wall Street analyst estimated that the fall of the euro in 2000 shaved 3 percent off total Standard and Poor 500 operating profits in the third quarter alone. The president of TRW lamented, “If I could report in euros, we would be having a bang-up year.” Unfortunately, this was not possible. In 2003, the euro increased in value. This was good news for U.S.-based firms selling in the euro area, but bad news for EU-based firms selling in the United States and reporting profits in euros. Volkswagen, for example, attributed a €1 billion fall in profits to the strengthened euro. One way or another, changing exchange rates affect firms engaged in international production.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".