Effects-Based Approaches to Operations: Canadian Perspectives
Bibliographic record
Abstract
There are currently three major theoretical approaches that dominate analyses and descriptions of military operations. They are Operational Art, Network Centric Warfare (NCW) (or Network Enabled Operations (NEOps) in the Canadian context), and Effects Based Operations (EBO). The concept of EBO is currently having a significant influence on Operational Art and NCW, as well as how operations are conceptualized in the new security environment. The EBO concept is emerging through discussions and papers within Defence Research and Development Canada (DRDC) jointly with other stakeholders in the Department of National Defence (DND); however, there are many ways of describing EBO in the literature and in practice. In order to fully understand the nature of EBO today and how it might evolve in the future, it is essential to understand the theoretical and historical origins of this subject, as well as how EBO is conceptualized and practiced by the CF. Since there has been no comprehensive examination of these concepts in a Canadian context, the Command Effectiveness and Behaviour Section at DRDC Toronto co-sponsored with the Canadian Forces Aerospace Warfare Centre (CFAWC) a two-day workshop to identify the issues related to EBO and to begin to establish the agenda for better understanding EBO. This report is the product of that workshop and it includes not only the main conclusions of the workshop, but also essays on EBO by workshop participants, and others.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".