Questioning, Asking and Enduring Curiosity: an Oral History Conversation between Julianne Nyhan and Willard McCarty
Bibliographic record
Abstract
This interview was carried out with Willard McCarty on Tuesday 27th March, 2012 in University College London. He recounts that his earliest encounter with computing was in the Lawrence Radiation Laboratory in Berkley where he worked with semi-automated scanning equipment for the Alvarez high-energy physics projects. After his dreams of becoming a physicist were thwarted he transferred to Reed College. There he did not have the opportunity to take formal training in computing; for the most part, Computer Science departments did not exist then. So, he learned to programme on the job with help from a talented physicist turned computer programmer named Bill Gates (no association with Microsoft). His first encounter with what we now call digital humanities was at the University of Toronto where he worked on the Records of Early English Drama project whilst undertaking a PhD on 17th century non-dramatic poetry. In 1984/5, as he was finishing his PhD, he accepted an academic support role at the Centre for Computing in the Humanities at Toronto, where he remained until 1996 when he accepted an academic post in King's College London. In Toronto he was keenly aware of the staff-faculty divide and the marginalised position of those who used computers in Humanities research. Nevertheless, the opportunities that the role brought to meet with a range of scholars interested in computing had a lasting influence on him. So too, with funding from the Social Sciences and Humanities Research Council of Canada he was able to undertake a research project on Ovid's Metamorphosis. He closes the interview by reflecting on his early involvement with the conference scene and people who have influenced him, from academics to his calligraphy teacher Lloyd Reynolds.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.043 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".