Bibliographic record
Abstract
Why do Canada’s military procurement projects often fall short of their primary goals? Otherwise known as a suboptimal result, defence acquisitions regularly fall short of established delivery schedules, accruing cost-overruns, sometimes resulting in cancellation of key materiel. One-hundred percent of the twenty-five Major Crown Projects at the Department of National Defence have experienced delays in achieving key milestones. Aside from cost, suboptimal results are injurious to Canada’s tri-force military. Fortunately, the matter has not gone unchecked. A fairly recent surge in procurement research has generated a critical mass of Canadian-focused literature. Preliminary research for this study shows a connection between suboptimal results and the organizations and personnel that populate procurement processes. Based on the bureaucratic politics model, a competitive interaction between uniquely conditioned policy players causes suboptimal delays and costs. Players orient outcomes to suit personal and organizational interests. The advantage of the bureaucratic politics model is the clarity with which it illustrates decision processes. Its simplistic structure serves as an ideal model for comparing three cases in Canadian procurement. Taken from the Department of National Defence’s Status Report on Transformational and Major Crown Projects, this study tests the bureaucratic theory against the Tank Replacement Project, the Joint Support Ship Project, and the ongoing project(s) to replace Canada’s CF-18 Hornets. The objective is to see if these cases share common findings contributing to suboptimality. The bureaucratic model assists the methodological goal of a structured, focused comparison. Two of the three cases demonstrate the competitive interaction between players as a factor in determining delays and cost-overruns. Although the Joint Support Ship Project included a host of unique players competing to determine decision outcomes, the factor that contributed to schedule slippage was the result of widespread agreement on a build in Canada approach. This consensus led to reliance on one underequipped shipyard to approach an overambitious project. Based on the overall study, three findings prevail. First, reports by bureaucratic institutions like the Parliamentary Budget Office and the Office of the Auditor General have tremendous political capital. Second, domestic production schemes are noble, but sometimes unrealistic. Third, competition for goods is always necessary.
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 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".