Bibliographic record
Abstract
In 1962, purdue University in West Lafayette, Indiana, in the United States opened a department of computer science with the mandate to offer master’s and doctoral degrees in computer science. Two years later, the University of Manchester in England and the University of Toronto in Canada also established departments of computer science. These were the first universities in America, Britain, and Canada, respectively, to recognize a new academic reality formally—that there was a distinct discipline with a domain that was the computer and the phenomenon of automatic computation. There after, by the late 1960s—much as universities had sprung up all over Europe during the 12th and 13th centuries after the founding of the University of Bologna (circa 1150) and the University of Paris (circa 1200)—independent departments of computer science sprouted across the academic maps on North America, Britain, and Europe. Not all the departments used computer science in their names; some preferred computing, some computing science, some computation. In Europe non-English terms such as informatique and informatik were used. But what was recognized was that the time had come to wean the phenomenon of computing away from mathematics and electrical engineering, the two most common academic “parents” of the field; and also from computer centers, which were in the business of offering computing services to university communities. A scientific identity of its very own was thus established. Practitioners of the field could call themselves computer scientists. This identity was shaped around a paradigm. As we have seen, the epicenter of this paradigm was the concept of the stored-program computer as theorized originally in von Neumann’s EDVAC report of 1945 and realized physically in 1949 by the EDSAC and the Manchester Mark I machines (see Chapter 8 ). We have also seen the directions in which this paradigm radiated out in the next decade. Most prominent among the refinements were the emergence of the historically and utterly original, Janus-faced, liminal artifacts called computer programs, and the languages—themselves abstract artifacts—invented to describe and communicate programs to both computers and other human beings.
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.024 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".