Bibliographic record
Abstract
Abstract The People’s Republic of China (PRC) has undergone a significant shift in its diplomatic practices, driven by the use of social media and new digital technology. This shift is a departure from the non-resistant, passive, and conservative approach that the PRC has used for the past two decades, and is now characterized as provocative, nationalistic, aggressive, and high profile. The motivations behind this shift are often attributed to the defence of ‘national interest’ and ‘territorial integrity’ from a realist perspective. However, this new form of diplomacy, known as ‘wolf-warrior diplomacy’, also taps into a deep reservoir of emotions, including resentment, frustration, anger, pride, and status deprivation. Before 2019, wolf-warrior diplomacy was present only at the margins of PRC diplomatic practice. However, the shift reflects an awareness of the tools of technology, the use of social media, informational technology, and AI at the PRC’s disposal, as well as a massive echo chamber of netizens (especially the 50 cent army). The shift came about because of a perceived strategical necessity rationale at the apex of power with President Xi Jinping’s belief system that wolf-warrior diplomacy not only helps maintain the privileged position of the Chinese Communist Party (CPC) but also consolidates his personal support.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".