Summary of the Roundtable “Setting up a DH Curriculum or Certificate” at the Annual Meeting of the Renaissance Society of America (Toronto, March 19, 2019)
Bibliographic record
Abstract
This summary is a short overview of a roundtable discussion that took place at the Renaissance Society of America on the topic of the structure and organization of a Digital Humanities curriculum. I invited two representatives of European and two of US curricula, which were split up respectively into one for Digital Art History and one for general Digital Humanities: Leif Isaksen (Professor of Digital Humanities, Exeter), Peter Bell (Junior professor for Digital Humanities, with a focus on Digital Art History, Erlangen-Nürnberg), Hannah Jacobs (Digital Humanities Specialist in the Wired! Lab for Digital Art & Visual Culture, Duke University), and Ashley Sanders Garcia (Vice Chair of the Digital Humanities Program, UCLA). Both of the European cases are recent implementations of new curricula, whereas the US-American had established courses. While established studies do exist in Europe, as for example at the University of London, they are still quite rare.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.176 | 0.075 |
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".