Bibliographic record
Abstract
Networked operations, which are based on the concept of Network-Centric Warfare (ncw), are at the heart of many ongoing transformation initiatives in Western militaries.While terms like ncw are frequently used in discussing both how operations should be conducted in the current and future security environments as well as how Western armed forces should be transformed, these terms are used by different people to mean different things.These differences in meaning result from the confusion in many circles as to what the concept of ncw actually entails.Therefore, it seems timely to address the concept of networked operations, especially as it relates to transformation, by focusing on its origins and how it relates to other concepts, like the Revolution in Military Affairs, operational art, manoeuvre warfare, Rapid Decisive Operations, and Effects-Based Operations.Without a clear idea of the context of the networked operations concept, decision-makers and military professionals may not understand the implications of their decisions and actions in planning and implementing transformation initiatives.The genesis of this book was a meeting called by Carol McCann at Defence Research and Development Canada (drdc) -Toronto in March 2005 to discuss the implications of the concept of networked operations, particularly ncw, for Canadian Forces (cf) operations in light of the increasing prominence of a Canadian version of networked operations -Network-Enabled Operations, or neops.Those attending the meeting were military officers, academics, and retired officers who were now in the academic community, all of whom had detailed knowledge of some aspects of networked operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.537 | 0.513 |
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".