The Problems of Public Procurement in Canada: Rule Layering, Strategic Purchasing, and Risk Aversion as a Toxic Stew
Bibliographic record
Abstract
Abstract This article analyzes persistent challenges in Canadian public procurement, examining in particular the federal government's complex, multi‐layered purchasing processes which have contributed to high‐profile failures, such as most recently the ArriveCan app and, over the past century, the majority of major military procurement projects. The primary issues found to contribute to these problems include excessive rule layering, unnecessary strategic purchasing complexities, bureaucratic risk aversion and political interference which together form a “toxic stew” leading to poorly designed processes hampering procurement efficiency and effectiveness. The article highlights three main strategic directions for improvement: streamlining procurement to increase efficiency, rethinking strategic purchasing to simplify decision‐making, and enhancing transparency and accountability for all projects to combat excessive caution and political interference. These proposals aim to address the key structural and political issues that have undermined procurement and help reform this key area of government activity.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".