Bibliographic record
Abstract
Network-centric operations (NCO) concepts and capabilities are central to Department of Defense (DOD) transformation efforts and are predicted by advocates to have wide-ranging impacts on the conduct of warfare and military forces. NCO concepts cover the entire military response to the Information Age, including ways of thinking, human and organizational behavior, and the networks the military uses across the tactical, operational, and strategic levels of warfare. In a broad sense, NCO is about harnessing networks and networked forces to create military advantages and capabilities. This paper first highlights the centrality of NCO to DoD transformation efforts by using examples from Joint Visions 2010 and 2020, the Office of the Secretary of Defense's Office of Force Transformation (OFT), and Service transformation documents to demonstrate the importance of NCO to DoD. Next, it examines NCO concepts to identify core characteristics and underlying capabilities levied on the supporting network. These sources of NCO thought come primarily from DoD authors; however, many other countries and alliances, including the United Kingdom, Canada, Australia, New Zealand, and NATO, are also interested in NCO-like concepts. The paper then analyzes several capabilities required of networks to determine some of the attendant requirements and challenges. This analysis includes potential impacts should networks fail to achieve the required performance or collapse under attack. These challenges are illustrated using examples from the author's experience on the CENTCOM/J6 staff during Operations Enduring Freedom and Iraqi Freedom (OEF and OIF). Finally, the analysis provides some recommendations to mitigate associated vulnerabilities introduced by relying upon networks and the promises of NCO.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".