Bibliographic record
Abstract
The forces that shaped Canada's digital innovations in the postwar period. After World War II, other major industrialized nations responded to the technological and industrial hegemony of the United States by developing their own design and manufacturing competence in digital electronic technology. In this book John Vardalas describes the quest for such competence in Canada, exploring the significant contributions of the civilian sector but emphasizing the role of the Canadian military in shaping radical technological change. As he shows, Canada's determination to be an active participant in research and development work on advanced weapons systems, and in the testing of those weapons systems, was a cornerstone of Canadian technological development during the years 1945-1980. Vardalas presents case studies of such firms as Ferranti-Canada, Sperry Gyroscope of Canada, and Control Data of Canada. In contrast to the standard nationalist interpretation of Canadian subsidiaries of transnational corporations as passive agents, he shows them to have been remarkably innovative and explains how their aggressive programs to develop all-Canadian digital R&D and manufacturing capacities influenced technological development in the United States and in Great Britain. While underlining the unprecedented role of the military in the creation of peacetime scientific and technical skills, Vardalas also examines the role of government and university research programs, including Canada's first computerized systems for mail sorting and airline reservations. Overall, he presents a nuanced account of how national economic, political, and corporate forces influenced the content, extent, and direction of digital innovation in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".