Trends in the Development of US–China Relations After the 2020 Presidential Election in the Context of the Information and Political Discourse of American Elites
Bibliographic record
Abstract
In a short period, from 2016 to 2020, China has transformed from the main trade and economic partner of the United States, during the years of Barack Obama’s presidency, to one of the leading opponents of the US administration. This article analyses the reasons for the growing tension in US–China relations and the trade war as the apogee of this confrontation considers the discourse of American political elites in the media regarding China’s participation and role in the demarcation between states and assesses the prospects of relations between the two countries under the Democratic administration of Joe Biden, with a focus on the information agenda in the United States. The quantitative results of the topic modelling analysis show that the ongoing ideological shift of discourse from the Democrats and lack of any discussion of trade negotiations resulted in 2022 demonstrate that the shift from the economic sphere to ideology has been completed. The tensions between China and the United States have transferred to the political-diplomatic stage with a new danger for the United States and NATO interests coming to the surface—Russia and its policy in Eastern Europe.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".