Bibliographic record
Abstract
Abstract How do states compete for status—i.e., an elevated position in the international order? Conventional wisdom suggests that states do so by enhancing their own status, such as by joining selective international institutions or winning wars. I theorize and test another strategy: reducing their competitor's status through delegitimation. By spreading information about the target's failure (i.e., character assassination), delegitimation can undermine the target's status in the eyes of third-party states and subvert the target's ability to form coalitions with said third-party states. I test my theory through a survey experiment in Canada, wherein select respondents were exposed to Chinese information campaigns about US failure in the Middle East. Exposure to delegitimation reduces the respondents’ assessment of US status, in turn reducing their (1) support for Canada to participate in joint military exercises with the United States and (2) assessment of US credibility in multilateral trade negotiations. I contextualize these results through a case study of Chinese delegitimation of US policy in Africa and its impact on African countries’ alignment with the United States. My analysis highlights the changing character of war: the mechanisms and effects of information warfare, including America's psychological operations, China's “three wars,” or Russia's active measures.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".