Bibliographic record
Abstract
Purpose- This paper aims to analysis and explore the causes of Biden's preference for the strategic competition approach to address China's threat. Design/methodology/approach- The paper relies on the offensive realism theory as the most relevant theory to examine the US polies and approaches towards China, or US-Sino strategic competition. In particular, the theory is suited to explore Biden's preference for the strategic competition approach, and not the hostile approach. As the theory contends that the great powers are rational actors do not engage in destructive wars if the balance of power is not in their favor. Findings- Since 2008 U.S perceives China as a most serious threat to its global hegemony. Obama and Trump's administrations have prioritized the strategic competition approach towards China, which aims at containing China's power smoothly without sliding competition to the level of severe hostility. Biden maintained this approach and also, reinforced it to avoid as much as possible major hostility with China. Through, for instance, rallying alliances and multilateralism. Based on that, the study suggests the U.S will prioritize this approach unless China upsets the existing balance by annexing Taiwan or taking full control of the South China Sea. Originality/value -The significance of the paper emanates from providing an in-depth analysis and explanation of the cause of the conflict or the cold war between China and the United States, and the main policies and approaches that the United States adopt to deal with China.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".