Prototyping the Renaissance English Knowledgebase (REKn) and Professional Reading Environment (PReE), Past, Present, and Future Concerns: A Digital Humanities Project Narrative
Bibliographic record
Abstract
The Renaissance English Knowledgebase (REKn) is an electronic knowledgebase consisting of primary and secondary materials (text, image, and audio) related to the Renaissance period. The limitations of existing tools to accurately search, navigate, and read large collections of data in many formats, coupled with the findings of our research into professional reading, led to the development of a Professional Reading Environment (PReE) to meet these needs. Both were conceived as necessary components of a prototype textual environment for an electronic scholarly edition of the Devonshire Manuscript. This article offers an overview of the development of both REKn and PReE at the Electronic Textual Cultures Laboratory (ETCL) at the University of Victoria, from proof of concept through to their current iteration, concluding with a discussion about their future adaptation, implementation, and integration with other projects and partnerships.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".