The Bureaucracy of Intervention: Explaining Canadian Armed Forces Deployment Variations 2014-2018
Bibliographic record
Abstract
After the closure of the Afghanistan mission in March 2014, the Canadian government was presented with a distinctive opportunity to “re-set” the overseas posture of its armed forces. Almost all personnel serving overseas were brought back to Canadian soil before the government announced several new operations. From this “re-set” derives the central research question of this dissertation: Why do states who are not threatened deploy their armed forces in various speeds and sizes? Competing scholars who study foreign and defence policy analysis have been unable to reach a consensus and explain why states like Canada decide to participate quickly in certain military coalitions (or not), or why the executive decides to contribute specific numbers of personal and resources to particular overseas missions (or not). Utilizing a critical small-n case study approach and focusing on Canada’s most recent military deployments to Iraq, Ukraine, and Mali, as well as the non-deployment of forces to Columbia, this research asks where deployment recommendations originate from within the bureaucracy and what is their impact on the executive. Using purposive sampling and relying on semi-structured interviews with select members of the Canadian Public Service and the Armed Forces, I combined Tsebelis’ veto player’s theory and Halperin and Clapp’s theory of bureaucratic politics to the Canadian federal bureaucracy to identify and explain the level of bureaucratic influence on post-Afghanistan non-combat interventions. My research found that federal bureaucracies acted as veto players - based on both departmental mandates and departmental self-interests - and were able to demonstrate substantial influence on the speed and size of CAF deployments, delaying their support until departmental interests were met. My research demonstrates that Bureaucratic Intervention Theory, when applied to a federal bureaucracy, has greater explanatory powers than had been previously understood, contributes to an enhanced understanding of the current executive decision-making process, and helps illustrate the bureaucratic consensus required to ensure future CAF deployments.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".