Factors affecting the evolution of manufacturing in Canada: An historical perspective
Bibliographic record
Abstract
Abstract This paper examines the factors that influenced developments in industry and manufacturing in Canada from the 17th to the 20th century. Although Canada's abundance of natural resources led to the development of primary industries in the 17th and 18th centuries, the manufacturing industry was not significant until the early 19th century. Four representative manufacturing industries are discussed to illustrate the overall trend in the chronological evolution of Canadian manufacturing in the 19th and 20th centuries. The role and impact of factors such as transportation, electricity, foreign investment, particularly by U.S. entrepreneurs, and government support for industry is reviewed to understand their impact on manufacturing as it has evolved to the present. It appears that these were indeed influential and thus are factors that other countries in a less developed stage of their manufacturing evolution may look to for directions. Our analysis also shows that Canadian manufacturing which began by producing simple items in small volumes due to geographical diversity and the absence of a large market, moved into the mass manufacturing age only in the 20th century. But in the 21st century due to competition from low labour cost countries Canada has moved back to customized manufacturing though in sophisticated goods such as aircraft manufacturing and biotech. While there are bright spots in Canadian manufacturing, recent studies also show that work needs to be done to produce more value added products and ensure Canadian manufacturing competitiveness in the global market place.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".