America’s “Narrative of Combating the COVID-19 Pandemic” in “Post-truth” Context
Bibliographic record
Abstract
Some US politicians and media, driven by the zero-sum Cold War mentality, ideological bias, and domestic political needs, have made no effort to politicize, stigmatize, and label the epidemic with the “post-truth” narrative logic of “promoting values and belittling the epidemic” in order to hide the institutional impotence and political incompetence exposed by the epidemic response and mitigate the impact of the comparison between “China’s governance” and “chaos in the United States.” The U.S. narrative of fighting COVID-19 was based on fabrications, fallacies, and hegemonic construction, which involved scapegoating others and assigning blame. The US “anti-epidemic narrative” is essentially a weapon to use fallacies to cut reality, fabricate history with lies, and suppress China with hegemony. The “anti-epidemic narrative” of the US is that it is dissatisfied with everything about China, regards China as being behind the times, and is hostile to both the Communist Party and Marxism. It is a denial of China’s strategy, direction, and system. The best method to combat the global public health crisis is to improve global public health governance, increase international cooperation against COVID-19, jointly build a “Silk Road for health,” and create a community of health for all people. Additionally, they contribute to revising the US “narrative against COVID-19.”
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".