Multinational operations: tactics, techniques, and procedures.
Bibliographic record
Abstract
Increasingly, Canada's military is being called upon to deploy into complex operational environments where it must deal with highly adaptive adversaries seeking to destabilize society through a variety of asymmetric means. Articulating this new paradigm, the Army's Land Operations 2021: Adaptive Dispersed Operations, identifies a security environment in which "...the likelihood of large force-on-force exchanges will be eclipsed by irregular warfare conducted by highly adaptive, technologically enabled adversaries ... intent less on defeating armed forces than eroding an adversary's will to fight." The document continues to explain...[that] turmoil will often occur in urban areas, with adversaries taking full advantage of the complex physical, moral and informational environments that large, densely populated cities provide."1\nIn order to succeed in this dynamic and complex battlespace, armed forces will have to focus upon intelligence-driven operations that are grounded in extensive knowledge of both the local populations and the belligerents. Indeed, to be of use, this knowledge must derive from an in-depth analysis of the background and motives of the enemy and the cultures they are seeking to overtake. Brigadier-General David Fraser, former Commander International Security Assistance Force (ISAF) Multi-National Brigade Sector South, Kandahar, Afghanistan, recently admitted: "I underestimated one factor -- culture." He when went on to lament: "I was looking at the wrong map -- I needed to look at the tribal map not the geographic map...Wherever we go in the world we must take into account culture."2 This forthright acknowledgement from an experienced and decorated warfighter is telling. It underscores the Canadian Forces' (CF) current lack of capability in what is quickly becoming the crucible of success in the modern battle space: the ability to effectively integrate Cultural Intelligence (CQ) into modern military operations.\nThis article will look at CQ and highlight examples of how it can be used as a force multiplier in military operations. It will then look at the current state of CQ within the Canadian military, and review what other nations are doing in the field. Finally, it will make recommendations as to how the CF can establish a capability that will remain relevant well into the future. However, in order to gain an appreciation of the potential of applying CQ to the battlespace, one must first comprehend the meaning of the concept and how it is applied within the military context.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".