The Next War: Indications Intelligence in the Early Cold War
Bibliographic record
Abstract
The threat of nuclear conflict loomed menacingly over the world during the Cold War. Early warning of an attack was a crucial focus for military and political intelligence. Intelligence networks in Canada, the United States, and the United Kingdom came together, forming a tripartite intelligence relationship dedicated to indications that the Cold War would turn hot. The Next War is the first full account of the development of the allied indications network. Timothy Andrews Sayle dives deeply into recently declassified documents to explore this previously hidden history. He traces the decisions and choices made by intelligence organizations in Canada, the United States, and the United Kingdom to coordinate their assessments despite different, sometimes conflicting, national agendas, ideological positions, and levels of trust. From early appreciations of the possibility of war with the Soviet Union to a formal agreement and communications network designed to link the intelligence establishments of Ottawa, London, and Washington, the tripartite intelligence relationship of the allied indications network established the basis for the close cooperation that continues to this day. The Next War widens our understanding of Cold War intelligence history through exemplary scholarship and extensive foraging within the documentary record. With its descriptions of the evolution of national indications intelligence structures and the diplomacy and debates between allied capitals this book explains Canada’s prominent role alongside its intelligence partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".