Getting Computers into Humanists’ Thinking: John Bradley and Julianne Nyhan
Bibliographic record
Abstract
This interview took place in Bradley’s office in Drury Lane, King’s College London on 9 September 2014 around 11:30. Bradley was provided with the interview questions in advance. He recalls that his interest in computing started in the early 1960s. As computer time was not then available to him he sometimes wrote out in longhand the FORTRAN code he was beginning to learn from books. One of his earliest encounters with Humanities Computing was the concordance to Diodorus Siculus that he programmed in the late 1970s. The printed concordance that resulted filled the back of a station wagon. The burgeoning Humanities Computing community in Toronto at that time collaborated both with the University of Toronto Computer Services Department (where Bradley was based) and the Centre for Computing in the Humanities, founded by Ian Lancashire. Aware of the small but significant interest in text analysis that existed in Toronto at that time and pondering the implications of the shift from batch to interactive computing he began work as a developer of Text Analysis Computing Tools (TACT). He also recalls his later work on Pliny , a personal note management system, and how it was at least partly undertaken in response to the lack of engagement with computational text analysis he noted among Humanists. In addition to other themes, he reflects at various points during the interview on models of partnership between Academic and Technical experts. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.033 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".