The Past as Future: The US Army's Vision of Warfare in the 21st Century"
Bibliographic record
Abstract
Today the US Army is engaged in the effort to learn the appropriate lessons from the wars it has been engaged in since the autumn of 2001 and to think through what type of force it needs to be, with what kinds of capabilities, in order to prepare for further future conflicts in the 21st Century. Estimating the character of future conflicts, and then preparing one’s forces appropriately, is not an easy task. A critical line of argument today is that the vision of future warfare the Army developed in the decade plus following the end of the Cold War left it ill-prepared for the wars it found itself conducting in Afghanistan and Iraq. In an article published in 2007, US Army Lt. Col. Paul Yingling very pointedly, and very boldly for a serving officer, contended that, “throughout the 1990s our generals failed to envision the conditions of future combat and prepare their forces accordingly.”1 The US Army’s operational experiences in the first decade of this century, particularly in the early years of the long conflict in Iraq, suggest that it marched eyes wide shut through the decade of the 1990s into the 21st Century.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".