HIV/AIDS, SARS, and COVID-19: the trajectory of China’s pandemic responses and its changing politics in a contested world
Bibliographic record
Abstract
The COVID-19 pandemic has revealed the contested politics of global health governance, though we still don't know enough about the dynamics of domestic pandemic responses, or about the relationship between the politics of those responses and the politics of global health governance, both of which have changed significantly in recent decades. Focusing on three cases (HIV/AIDS, SARS, and COVID-19) of cross-border infectious diseases, this article explores the trajectory of China's pandemic responses in the context of globalization. Attending to changing politics at domestic, international, and global levels, I argue that those responses have been a complex combination of China's domestic politics (e.g., priorities, institutions, leadership, and timing), its international relations (especially with the US), and its engagements with global health governance. It is concluded that the increasing divergence of pandemic responses in a time of ubiquitous global health crisis demands urgent attention to the connections (including contestations) between domestic pandemic responses and the evolvement of global health governance from a broader perspective that considers changes in geopolitics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".