The premise of computer science: Establishing modern computing at the University of Toronto (1945--1964)
Bibliographic record
Abstract
This dissertation explores the introduction and acceptance of electronic computers at the University of Toronto, from the first vague intentions of 1945 to the creation of the first Department of Computer Science in Canada that offered a doctoral degree in 1964. It is not the aim of this project to provide a history of computer science, per se, as the discipline did not coalesce until after most of the events discussed herein. Instead, as the historical literature concerning computer science is still underdeveloped, this pre-history provides both a useful case study and a foundation for further research on the history of computing and computer science in Canada. In the second half of the 1950s, less expensive and more reliable commercial computers appeared on the market and other Canadian organizations began making plans to acquire one of their own. As the Toronto computing centre was self-financed through the sale of computer time and federal grants, the changing environment reduced the national significance of the centre and forced a reevaluation of values. Two interrelated plans were made to regain its fortune: to obtain the most powerful computer in Canada, and establish a new, autonomous academic department dedicated to computing research. Success was elusive until the early 1960s, and neither concluded in the expected manner. The story begins shortly after World War II, when a group of professors with an interest in modern computing devices petitioned the university and several federal agencies for funding to build or buy an electronic computer. Though located in Toronto, it was hoped that all Canadian scientists could use the new machine for their computations. There were setbacks, including a failed attempt to design and construct a full-scale electronic computer, and successes, ironically involving older, premodern equipment. In 1952, the first electronic computer in Canada was installed at Toronto, though few knew how to use it. With assistance from programmers at Manchester University, the Toronto computing centre mastered the computer and made it available to the rest of the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".