By, With, and Through: Capacity Building and the Canadian Armed Forces
Bibliographic record
Abstract
In November 2017, the Government of Canada announced a new capacity-building mission in support of the United Nations. When considering the employment of the Canadian Armed Forces (CAF) around the world, capacity building currently represents the vast majority of CAF operations. This raises two important questions. Why does the Canadian government favor capacity building operations? What drives the Canadian public support for international operations and does capacity building meet these expectations? This monograph argues that capacity-building missions are uniquely suited to meet the current government's intent and the expectations of Canadians for the use of the Canadian military. By understanding why the government and people favor one type of mission over another, the CAF's leadership can shape military advice on the employment of military forces, drive force generation requirements, and provide a most likely scenario for the development of training, doctrine, and organizational structures. To support this thesis, this monograph examines the government's intent for the CAF as stated in the foreign and defense policies. Research reveals five key objectives for the use of military force overseas: contributing to global stability, strengthening multilateral institutions, reinforcing Canadian-US military relationships, countering global threats, and maintaining a combat credible force. Using academic papers, research polls, and reports on CAF operations, the monograph also examines how Canadians influence government policies through public support in terms of three concerns. Canadians want to understand clear national security links and military objectives. They also judge CAF operations in terms of acceptable costs of risk of casualties combined with the length of the mission. The monograph concludes with why capacity-building best reconcile the Canadian government's intent while maintaining public support.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".