Bibliographic record
Abstract
A large literature has emerged on intelligence and war which integrates the topics and techniques of two disciplines: strategic studies and military history. The literature on intelligence and war is divided into theory and strategy; command, control, communications, and intelligence (C3I); sources; military estimates in peace; deception; conventional operations; strike; and counter-insurgency and guerilla warfare. Sun Tzu treats intelligence as central to all forms of power politics, and even defines strategy and warfare as “the way of deception.” On the other hand, C3I combines signals and data processing technology, command as thought, process and action, the training of people, and individual and bureaucratic modes of learning. Since 1914, the power of secret sources has risen dramatically in peace and war, revolutionizing the value of intelligence for operations, especially at sea. The strongest area in this study is signals intelligence. Meanwhile, the relationship of intelligence with war, and with power politics, overlaps on the matter of military estimates during peacetime. The literature on operational intelligence is strongest on World War II. However, analysts have particularly failed to differentiate the effect of intelligence on operations, from that on a key element of military power since 1914: strike warfare. In counter-insurgency, many types and levels of war and intelligence overlap, which include guerillas, conventional and strike forces, and politics in villages and capitals.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".