Middle-Power Alignment in the Free and Open Indo-Pacific: Securing Agency through Neo-Middle-Power Diplomacy
Bibliographic record
Abstract
executive summary: This article explores how middle powers in the Indo-Pacific are engaging in a new type of diplomacy, one that includes lobbying, insulating, and rulemaking in the realms of security, trade, and international law, to protect their national interests from Sino-U.S. strategic competition. main argument The change in the power balance associated with China's re-emergence as Asia's largest economy has brought concerns about Sino-U.S. strategic competition and raised questions about U.S. leadership in the Indo-Pacific region among many U.S.-aligned middle powers, such as Australia, Japan, Canada, and India. Specific challenges that China is creating include fomenting instability in the maritime domain, fracturing the openness of the emerging digital economy, and practicing coercive economic behavior, to which middle powers are especially vulnerable. Therefore, the Indo-Pacific's middle powers are aligning to adapt to these changing dynamics and transforming their diplomacy and cooperation into "neo-middle-power diplomacy." This new type of diplomacy is proactive and engages in behavior that includes lobbying, insulating, and rulemaking in the realms of security, trade, and international law. It aims to ensure that middle powers' interests are not deleteriously affected by the Sino-U.S. rivalry. policy implications • Like-minded middle powers should actively seek out alignment partners inside and outside the region based on a convergence of interests. U.S. involvement is preferred but not a prerequisite for alignment and cooperation. • Middle powers should focus cooperation on key areas based on the synergy of their respective comparative advantages. Ideally, these would stress capability-based contributions, such as intelligence gathering, rather than the capacity of the resources available for cooperation. Examples include regularized humanitarian and disaster-relief activities; maritime cooperation in the East and South China Seas, Taiwan Strait, and Indian Ocean; and joint transits in the Indo-Pacific. • Middle powers should prioritize their interests in free trade and "data free flow with trust" in the digital economy to both provide economic incentives to emerging states in the region and develop trade safety-net agreements that will allow them to support each other when subject to economic coercion.
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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 | 0.003 | 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 | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".