Why the Five Eyes? Power and Identity in the Formation of a Multilateral Intelligence Grouping
Bibliographic record
Abstract
Abstract Adopting an analytically eclectic approach that draws on theories of realist bargaining, identity, and socialization, this article investigates the early Cold War origins of the Five Eyes intelligence grouping (the United States, Canada, Great Britain, Australia, and New Zealand). An understanding of identity grounded in culture suggests a natural process of international intelligence community building, but this was not the case with the Five Eyes. The formation of the grouping was not preordained. Although Anglo-Saxonism was a necessary condition, it was not sufficient. In addition to being able to provide valuable sites for signals intelligence collection, aspiring members had to be seen as staunchly anti-Communist (and therefore politically trustworthy) by the United States in order to become full members of this exclusive community. Early postwar concerns over the political loyalties and secrecy protection regime of the Australian government prompted the British to initiate a process of socialization aimed at bolstering its affiliate's security institutions and practices and guaranteeing its own access to U.S. secrets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".