The National Shipbuilding Procurement Strategy: Securing Canada's Future Naval Shipbuilding Industry and Maritime Sovereignty
Bibliographic record
Abstract
The Government of Canada announced the National Shipbuilding Procurement Strategy (NSPS) in 2010. This paper seeks to inform the debate about the future of naval shipbuilding industry in Canada within the framework of NSPS. Developing, realizing and sustaining the shipping industry are not just a major industrial challenge. It also presents a major strategic opportunity to capitalize on substantial government investment in this sector. Lessons from Canada’s own experience as well as successful policy from selected countries are investigated. Those countries have thriving naval shipbuilding industries, forward planning and long-term commitment to a naval procurement strategy. To secure maritime sovereignty and efficient shipbuilding procurement strategy, a series of recommendations are presented in this study: continuity of naval shipbuilding activities beyond NSPS project, establishing value chains for the national shipbuilding industry, a good governance structure and sound project management. NSPS approach to naval procurement will continue to be a major step in modernizing the Royal Canadian Navy and the Canadian Coast Guard. With continuous commitment and forward-looking policy from the government of the day, supported by good governance and efficient project management, the success of NSPS will lay the foundation for Canada’s future naval shipbuilding industry and maritime sovereignty.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".