Bibliographic record
Abstract
On June 16 and 17, 2010 digital medievalists from many countries gathered at Barnard College, Columbia University in New York to discuss the implications of new digital technologies available to us for teaching and research. The event was held in honor of our esteemed colleague, Prof. Delbert Russell, who is now professor emeritus at the University of Waterloo. Together with Hannah Fournier (emeritus, University of Waterloo) and Jean-Philippe Beaulieu (University of Montreal), Delbert Russell was one of the founding members of the now internationally recognized MARGOT group, housed at the University of Waterloo. Prof. Russell was one of the early adopters of the digital humanities that John Unsworth refers to in his introduction. Already in the early 90s Delbert experimented with software originally written for the online Oxford Electronic Dictionary to adapt it to his goal of building a transcription database of otherwise inaccessible literary texts written by early modern French female authors. His desire to make available transcriptions of medieval texts to the broader public then led him to the development of an extensive database of medieval saints’ lives. This database of thirteen saints’ lives is used by many students and scholars today.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.480 | 0.274 |
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".