Strategies Available to Companies in Political Disputes: A Study of Boeing's Problems in the U.S.-China Political Confrontation
Bibliographic record
Abstract
Since the disintegration of the Soviet Union, the world's second largest economy at the time, the ever-growing China has begun to attract the attention of the United States. The United States has used various methods such as trade wars and wooing China's neighboring countries to contain China's development, and at the same time, the geopolitical conflict between China and the United States has become increasingly fierce. While the U.S. approach has curbed China’s economic growth, it has also adversely affected U.S. companies such as Boeing that have cooperated with China. This paper analyzes the impact of geopolitical tensions on Boeing and what Boeing can do to reduce the impact of geopolitics on the Boeing commercial aircraft market. To answer this question, this paper conducted a market position analysis of Boeing, studied official Boeing reports and other papers, and analyzed Boeing in terms of market and non-market aspects. The results show that U.S.-China geopolitical conflict has reduced Boeing's trade with China. The results also suggest that there is hope for a improve of Boeing's relationship with China if Boeing uses appropriate market strategies as well as non-market strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".