Economic Coercion and Grey Zone Competition: Reassessing the China-Australia Case
Bibliographic record
Abstract
Economic coercion is recognized as a major policy challenge for global leaders. The case of Beijing’s use of economic coercion against Australia (2020–2024) in response to bilateral tensions holds important insights. Research shows that China’s coercion efforts failed in two ways: the total costs to Australia’s economy were smaller than expected, and Canberra did not change pre-existing policies that triggered the coercion. Failure in this case is attributed to the ability of markets to adjust. Building on this research, we argue that while markets adapted relatively well in the Australia-China case, coercion still produced significant and concentrated subnational costs that differentially impacted Australian state economies. This resulted in political pressure and destabilization effects on Australian federal politics, influencing the provision of concessions favourable to Beijing during bilateral negotiations to restore trade relations. Informed by a novel geoeconomic and hybrid warfare framework, this article therefore offers new insights on the political effects of economic coercion in democracies. Our findings suggest that weaponization of trade can serve as an effective geoeconomic strategy for grey zone/hybrid warfare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".