Bibliographic record
Abstract
Anglo-Saxonists have always been well represented in the field of Digital Humanities, and perhaps the foremost among these has been The Dictionary of Old English. As we use the Dictionary and Corpus today, however, with their impressive modern interfaces and rapid search facilities, it is easy to forget that this project was first conceived in the 1960s when computing was paid for by the hour and the cutting edge in data storage was reel-to-reel magnetic tape. Despite these and other significant limitations, the Dictionary team chose to use computing technology from the very start, producing both the corpus and the dictionary itself in digital form, and they have managed to sustain this over some forty years. This achievement is a significant one, particularly as concerns about longevity of digital resources are still current, and so the lessons learned in this project are relevant to many of us now. These lessons are the ultimate subject of this paper, which will begin by considering the Dictionary of Old English Project and its development in the context of computing and digital humanities before discussing some uses and limitations of the Dictionary and Corpus and finally noting some brief lessons for large digital projects in general.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.053 |
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".