REED London Online: A Year in the Making
Bibliographic record
Abstract
In late 2017 Diane Jakacki was awarded a Mellon/NHPRC Planning Grant for a Records of Early English Drama (REED) Project, focussing on three collections of records from London, England, covering the years 1400-1558. Throughout 2018, the REED London Team has been testing the boundaries of production within the Canadian Writing Research Collaboratory (CWRC)'s digital publishing environment. Working collaboratively with a team that spans the US, the UK, and Canada, we have paved the way for a digital edition of one of the three collections - the Inns of Court Records. This paper will outline the challenges of doing digital scholarly production at a distance, and will highlight attempts to take advantage of CWRC's infrastructure to support the entire process: from marking up records using the TEI, to the creation of entity lists and relationships between them, resulting in data formatted in the Resource Description Framework (RDF), the language of the semantic web. In addition to outlining the process required for implementing our framework for the creation of digital editions, we will showcase our records as displayed in CWRC's Dynamic Table of Contexts. This web-based reading environment allows users to customize their selected records with tags and annotations, and helped the REED London team to conceive of ways that different audiences for our records (Shakespearean scholars or economic historians, for example) might explore our digital collections. We will showcase two sets of records, one of correspondence regarding events at court and the other accounts of payments for masque materials, to demonstrate how different scholars might draw upon the rich history in these records.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.035 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.106 | 0.042 |
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".