The Past, Present and Future of Anglosphere Security Networks: Constitutive Reduction of a Shared Identity
Bibliographic record
Abstract
This chapter maps a new terrain of public policy collaboration in the Anglosphere. Over the past decade, ministers, mandarins and their deputies from across core Anglosphere states – Australia, Canada, New Zealand, the United Kingdom and United States – have begun to establish and entrench a new class of transgovernmental networks with their counterparts. Though such networks rarely come into public view, they are significant sources of the ideas and information used to forge domestic public policy. Increasingly, moreover, these networks are turning informal cooperation into distinctive quasi-institutional arrangements. This chapter contributes to new literatures in International Relations and Public Policy exploring the underlying normative ideas that contribute to transnational governance. Drawing from public statements from network participants, it considers the dynamics and political implications of three specific network cases: the Quintet of Attorneys-General, the Five Country Ministerial and the Five Country Conference. Here it is found that Anglosphere institutions are pursuing ever-deeper collaborative ‘transgovernmental’ strategies to overcome nascent global threats to national interests. These are cohered by a series of appeals to a shared construction of the Anglosphere’s identity, its globality and the threats it faces.
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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 | 0.002 |
| 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 | 0.025 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".