Bibliographic record
Abstract
Since its emergence in 1998, the concept of Network Centric Warfare (NCW) has become a central driver behind America's military transformation and seems to offer the possibility of true integration between multinational military formations. Even though NCW, or variations on its themes, has been adopted by many armed services, it is a concept in operational and doctrinal development. It is shaping not only how militaries operate, but, just as importantly, what they are operating with, and potentially altering the strategic landscape. This paper examines how the current military dominance of the US over every other state means that only it has the capacity to sustain military activity on a global scale and that other states participating in US-led coalitions must be prepared to work in an interoperable fashion. It explores the application of computer networks to military operations in conjunction with the need to secure a network's information and to assure that it accurately represents situational reality. Drawing on an examination of how networks affected naval operations in the Persian Gulf during 2002 and 2003 as conducted by America's Australian and Canadian coalition partners, the paper warns that in seeking allies with the requisite technological capabilities, but also those that it can trust with its information resources, the US may be heading into a very secure digital corner.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".