Monopoly of Force: The Nexus of DDR and SSR
Bibliographic record
Abstract
Abstract : If experience is any guide, it is safe to say that the next decade will be as full of surprises as the past decade. There is no doubt we will be surprised, so our job is to be prepared for the unexpected so that when it arrives, we have the fewest regrets. The Joint Operating Environment (JOE) is U.S. Joint Forces Command's review of possible future trends that present significant security challenges and opportunities for the next quarter-century. From economic trends to climate change, from cyber attacks to failed states, the JOE outlines future disruptions and examines the implications for our national security in general and for the joint force in particular. These implications, plus current operations, inform the concepts that drive our Services' adaptations and the environments within which they will operate. Successful adaptation is essential if our leaders are to have the fewest regrets when future crises erupt. In our guardian role for the Nation, it is natural that we in the military focus more on the security challenges and threats than on emerging opportunities. Nonetheless, there are opportunities worthy of serious consideration, and it is our responsibility to reflect on those as well. This book, Monopoly of Force, highlights an area of opportunity we should all be interested in--one that, if done right, can save lives and resources and help short-circuit the cycle of violence in regions where conflict abounds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".