Bibliographic record
Abstract
Patterns of Change. The DEEDS Database, Topic Modeling and Network Analysis in English Medieval Charters, and the Future of Digital DiplomaticsThis paper describes the challenges of working in digital diplomatics over the past half century.In the 1970s, prior to the introduction of the laptop, digital research was performed on mainframe computers and the relational database was unknown.Microfiche replaced microfilm as a standard means of reproducing large numbers of images in a still physical medium.Data transfer was by dial-up telephone connections.The 1980s saw the foundation of the journals History and Computing, and Le médiéviste et l'ordinateur, both of which appeared regularly into the early twentieth century when the field broadened to make way for the Journal of Digital Humanities.2011 saw the on-line launch of the DEEDS Project in Toronto featuring a database of the rare English charters from the period 1066-1307 that were issued with dates.Using statistical applications, the DEEDS 'Dater' was subsequently developed to attribute dates to the 97% of documents from the period which are undated.Topic modeling, using Latent Dirichlet Allocation applied to nearly seventeen thousand internally dated charters, has provided clear indications of diplomatic variance which in turn reflect major societal change across the kingdom.Further examination of individual topics explains why the occasional charter was dated, while heat mapping of common diplomatic terms points to disruptions caused by political confrontations.Future directions in Humanities computing see the abandonment of stand-alone databases in favour of their integration.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.465 | 0.233 |
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".