Video-gaming, Paradise Lost and TCP/IP: an Oral History Conversation between Ray Siemens and Anne Welsh
Bibliographic record
Abstract
This extended interview with Ray Siemens was carried out on June 21st at Digital Humanities 2011, Stanford University. It explores Siemens' early training and involvement in the field that is now known as digital humanities. He recalls that his first experience with computing was as a video gamer and programmer in high school. He had the opportunity to consolidate this early experience in the mid-1980s, when he attended the University of Waterloo as an undergraduate in the department of English where he undertook, inter alia, formal training in computing. He communicates strongly the vibrancy of the field that was already apparent during his graduate years (up to c. 1991) and identifies some of the people in places such as the University of Alberta, University of Toronto, Oxford, and the University of British Columbia who had a formative influence on him. He gives a clear sense of some of the factors that attracted him to computing, for example, the alternatives to close reading that he was able to bring to bear on his literary research from an early stage. So too he reflects on computing developments whose applications were not immediately foreseeable, for example, when in 1986 he edited IBM's TCP/IP manual he could not have foreseen that by 1989 TCP/IP would be firmly established as the communication protocol of the internet. He closes by reflecting on the prescience of the advice that his father, also an academic, gave him regarding the use of computing in his research and on his early encounters with the conference scene.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".