Breaking All the Rules: Information Technology Procurement in the Government of Canada
Bibliographic record
Abstract
Abstract The Government of Canada has recently faced intense parliamentary and public scrutiny of the role played by private contractors in its information technology (IT) projects, most notably in the case of the ArriveCAN application. With these ongoing investigations as its backdrop, this article analyzes patterns in federal government IT procurement between 2017 and 2022, drawing on a comprehensive analysis of the federal contracting open dataset. We reveal that the federal government betrays accepted best practice in modern government IT procurement on several key dimensions, including on contract values and lengths; on the diversity of suppliers; on the source of IT expertise; and in the management of intellectual property. We argue that the Canadian approach to IT procurement is an historically overlooked but crucial driver of its failing digital reform efforts. We conclude by turning to IT procurement policy reforms gaining traction outside Canada that may help the Government of Canada improve how it buys and deploys IT going forward—a task we argue is essential if the government wants to avoid future IT contracting scandals and deliver on its long‐standing promise of digital era modernization.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".