Purchasing Power: Explaining Variation in the Canadian Armed Forces’ use of Contracted Services
Bibliographic record
Abstract
This dissertation examines why the Canadian military’s use of privatized defence services has varied over time. In answering this question, the hypothesis tested is that variation in the use of Private Military and Security Companies by the Canadian military has been driven by changes to the size and internal allocation of the defence budget. Drawing on the concept of constrained optimization, this hypothesis asserts that defence officials have attempted to optimize the allocation of the defence budget between its constituent spending categories of Personnel, Operations and Maintenance and Capital. Variation in the extent of service contracting has been influenced by changes to the size of the defence budget and constraints on its allocation. The dissertation reviews the history of changes to the size and allocation of the defence budget and the historical use of service contracts by the Department of National Defence (DND). It then examines four case studies that provide focused examinations of significant shifts in DND’s use of service contracts. Three of these cases examine increases to the use of service contracting: the Alternate Service Delivery program; In-Service Support contracting; and Operational Support Contracts. The fourth case study, the 2012 Service Contracting Cut, examines a decrease in the use of service contracts. This research found that constraints on DND’s ability to spend money on Personnel have been the most consistent cause of variation in the use of service contracts, but this variation was most significant when Personnel constraints were combined with budget cuts. Whether the combination of constraints on Personnel and budget cuts led to an increase or decrease in the use of service contracts has depended on the constraints on the other two major categories of defence spending (Capital or Operations and Maintenance).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".