Strategic hedgers? Australia and Canada's defence adaptation to the global power transition
Bibliographic record
Abstract
The intensification of rivalries between the US and China, and, in recent years, between the US and Russia, has deeply affected how middle powers relate to these great powers. Scholars have argued that middle powers are increasingly adopting “hedging” strategies to maximize their benefits and limit the consequences of the great power competition for their security and status. This paper revisits the concept of hedging and assesses whether two prominent US allies—Australia and Canada—have resorted to hedging in place of conventional alternatives like bandwagoning and balancing. The paper systematically compares Australia's and Canada's threat perceptions and defence policies to ascertain whether they have shifted their policies in the wake of the US's relative decline. Since our study began, in 2008, we have found instances where the two allies resorted to hedging. However, evidence shows that when pressured to make a choice, Australia and Canada have closed ranks with the US against revisionist powers. Our paper suggests that threat perceptions play a fundamental role in this. Going forward, it would suggest that the US is in a stronger position than commonly assumed. As the competition between Washington and revisionist great powers increases, the former's ability to build credible coalitions is expected to improve as it will rely on more dependable allies.
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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 | 0.001 | 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 | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".