There Had to Be a Better Way: John Nitti and Julianne Nyhan
Bibliographic record
Abstract
This oral history conversation was carried out via Skype on 17 October 2013 at 18:00 GMT. Nitti was provided with the core questions in advance of the interview. He recalls that his first encounter with computing came about when a fellow PhD student asked him to visit the campus computing facility of the University of Wisconsin-Madison, where a new concordancing programme had recently been made available via the campus mainframe, the UNIVAC. He found the computing that he encountered there rather primitive: input was in uppercase letters only and via a keypunch machine. Nevertheless, the possibility of using computing in research stuck with him and when his mentor Professor Lloyd Kasten agreed that the Old Spanish Dictionary project should be computerised, Nitti set to work. He won his first significant NEH grant c.1972; up to that point (and, where necessary, continuing for some years after) Kasten cheerfully financed out of his own pocket some of the technology that Nitti adapted to the project. In this interview Nitti gives a fascinating insight into his dissatisfaction with both the state and provision of the computing that he encountered, especially during the 1970s and early 1980s. He describes how he circumvented such problems not only via his innovative use of technology but also through the many collaborations he developed with the commercial and professional sectors. As well as describing how he and Kasten set up the Hispanic Seminary of Medieval Studies he also mentions less formal processes of knowledge dissemination, for example, his so-called lecture ‘roadshow’ in the USA and Canada where he demonstrated the technologies used on the dictionary project to colleagues in other universities.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".