Bibliographic record
Abstract
D onald Rumsfeld was right. Force transformation works. The techniques that led to the initial victories in Afghanistan in 2001 were precisely those that produced success in Libya in 2011. 1 Small-scale deployments of special forces backed by precision strike and deep attack capabilities used to support an allied indigenous armed group proved an effective military tool for achieving specific strategic outcomes. In contrast, the results of large-scale troop deployments as part of counterinsurgency (COIN), stabilization and nation-building activities over the past 1ten0 years in Iraq and Afghanistan have been less definitive. Despite intensive investment in blood, treasure, and military effort, the precise long-term outcomes of these two campaigns remain unclear and will be open to debate for years to come. This challenging operational experience has, however, highlighted some necessary and enduring truths about the use of military force. Despite great advances in military technology and the increasing sophistication with which organized violence can be applied in a range of situations, all warfare remains characterized by uncertainty; there exists no silver bullet that can guarantee enduring political success from the barrel of a gun. 2 The approaching end of the combat mission in Afghanistan in 2014 presents a potential watershed for the way in which the United States and her coalition partners, including Great Britain, seek to apply military power considering what has been learned in the first decade of twenty-first century conflict. This moment is significant in two respects. First, it provides a full stop (temporary or otherwise) to the recent western experience of large-scale, effortintensive, counterinsurgency operations. Second, it brings with it the potential
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".