Now What?: The Digital Medievalist Project and the Discovery of Best Practice
Bibliographic record
Abstract
Invited lecture (HTML slide deck) delivered at the SSHRC/University of Calgary ITST Summer Institute, 26 May 2004, given on behalf of the Digital Medievalist Project. The talk introduces the Digital Medievalist Project — its nature, the problem that led to its formation, its current state and goals, and how medievalists working with digital media can benefit from and contribute to it. It presents the Project as a community of practice for the discovery and sharing of best practice in digital medieval scholarship, with worked examples drawn from projects such as the Electronic Beowulf and the Electronic Cædmon's Hymn. This record contains the original HTML slide deck (zipped) and a slide-by-slide PDF rendering (title page, slides 1-13, and contacts). The PDF versions of the slides were generated by Claude Code from the HTML originals.
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.
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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.080 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".