Questioning, Asking and Enduring Curiosity: an Oral History Conversation between Julianne Nyhan and Willard McCarty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it