The United States facing allies’ populist blackmail: Why the Philippines and Turkey threatened to realign with China and Russia
Bibliographic record
Abstract
Abstract Given China and Russia’s increasingly aggressive behaviour, balance of threat theory posits that formal US allies should close ranks behind the United States. The literature on alliance politics reinforces this logic by showing that alliances deter aggression and reduce the occurrence of war. Recent developments, however, have somewhat undermined these claims, as the president of the Philippines, Rodrigo Duterte, and the president of Turkey, Recep Tayyip Erdogan, publicly threatened to break ranks with Washington and to realign with China and Russia respectively. How can we make sense of such defiant behaviour? This article argues that populist blackmail elucidates this phenomenon and compares it to three alternative propositions: conventional bandwagoning, bandwagoning for profit, and hard hedging. Based on empirical evidence, the article reveals that the provocative statements of Duterte and Erdogan were not a genuine push for realignment with Beijing and Moscow, but rather political strategies designed to enhance their bargaining power with Washington in the hopes of securing certain concessions, while simultaneously galvanising domestic support to justify their raison d’être and to secure their hold on power. Furthermore, the article infers that two concomitant factors – political grievances and the perceived lack of security assurance – propelled both presidents to resort to blackmail.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".