Trading Stories: an Oral History Conversation between Geoffrey Rockwell and Julianne Nyhan
Bibliographic record
Abstract
This extended interview with Geoffrey Rockwell was carried out via Skype on the 28th April 2012. He narrates that he had been aware of computing developments when growing up in Italy but it was in college in the late 1970s that he took formal training in computing. He bought his first computer, an Apple II clone, after graduation when he was working as a teacher in the Middle East. Throughout the interview he reflects on the various computers he has used and how the mouse that he used with an early Macintosh instinctively appealed to him. By the mid-1980s he was attending graduate school in the University of Toronto and was accepted on to the Apple Research Partnership Programme, which enabled him to be embedded in the central University of Toronto Computing Services; he went on to hold a full time position there. Also taking a PhD in Philosophy, he spent many lunch times talking with John Bradley. This resulted in the building of text analysis tools and their application to Hume's Dialogues Concerning Natural Religion, as well as some of the earliest, if not the earliest, conference paper on visualisation in the digital humanities community. He reflects on the wide range of influences that shaped and inspired his early work in the field, for example, the Research Computing Group at the University of Toronto and their work in visual programming environments. In 1994 he applied, and was hired at McMaster University to what he believes was the first job openly advertised as a humanities computing position in Canada. After exploring the opposition to computing that he encountered he reflects that the image of the underdog has perhaps become a foundational myth of digital humanities and questions whether it is still a useful one.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".