Bibliographic record
Abstract
“Everything in war is very simple,” Prussia’s premier military theorist, Carl von Clausewitz, famously stated, “but the simplest thing is difficult.” Strategy falls into that description. Almost no other term in military terminology has been so much and so often abused. Even the most fleeting scanning of major journals and newspapers, the briefest listening to national news casts, reveals a horrendous application of the term: the “strategy” of crossing a desert, the “strategy of storming a hill, the “strategy” of pacifying a village, the “strategy of securing a road, the “strategy” of winning the hearts and minds of indigenous populations—these are but a few of the misapplications of the term with which we are constantly bombarded by both reporters and so-called experts in the field. Not that the military has been much better in applying the term: two wellknown modern commanders, Erich Ludendorff in World War I and Bernard Montgomery in World War II, never quite understood it either; the former thought of strategy as the act of merely punching a hole in the enemy’s lines, while the latter cautioned his staff that strategy was strategy only if and when he, Montgomery, said it was.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".