Prospects for AUKUS Expansion under the Second Trump Administration
Bibliographic record
Abstract
The article is devoted to the analysis of the prospects for the expansion of AUKUS through the inclusion of new members, as well as the alliance’s overall activity following the return of Donald Trump’s administration to power. The authors examine recent trends and outcomes of cooperation within AUKUS framework and conclude that the alliance’s agenda generally aligns with U.S. interests under D. Trump, despite such challenges as the slow pace of nuclear submarine construction and what the Trump administration sees as insufficient Australian defense spending. At the same time, AUKUS members and potential candidates for joining the alliance hold different views on its expansion and activities: positive (Canada), neutral (South Korea, Japan), mostly critical (New Zealand), and ambivalent (Australia). The article pays special attention to how AUKUS is perceived by the Pacific Island States, which are concerned about the alliance’s nuclear component, environmental risks, and the lack of regional consultation. The authors conclude that, despite current U.S. support, the long-term sustainability of AUKUS will depend on the ability of its members to share responsibilities and find mutually beneficial solutions for implementing future projects under the Pillar 2 agenda.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".