If the Cold War Had Turned Hot by John Curry
Bibliographic record
Abstract
A perspective on the potential NATO and Warsaw Pact confrontation in the early 1970's Having played a number of wargames used by the West during the cold war for training the military, I started to consider the larger picture. Rules such as Contact! (Canadian Army Wargame Rules- 1980) or Dunn Kempf (American Army Wargame Rules 1977-1997) give a feel for the land war on a tactical level, but they do not say much about the wider perspective. The British Army Tactical Wargame rules (1956) offer a little more insight. They demonstrated that at that time, senior British Army Officers (and presumably American, if not other nations) trained on the wargames table top to stop the Russian hordes advancing the liberal use of tactical nuclear weapons. The rules indicated that a recce screen would have been used to identify the enemy axis of advance and then communication nodes being used by the advance would have been the nuclear targets. From the perspective of 2008, it is possible to start looking back on the cold war and to evaluate the military possibilities with some certainty. This article breaks down the complex strategic picture into 'surprise', 'the air war', 'the naval war ' and finally 'the land war'. Surprise One of NATO's public fears (and that of the Russians) was a surprise attack. Both were
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".