Current issues in making digital editions of medieval texts—or, do electronic scholarly editions have a future?
Bibliographic record
Abstract
It has been more than ten years since the first digital editions began to see the light of day. This article examines the current state of and future possibilities for the digital critical edition. Despite great promise, the article argues, digital editions have not been as successful with the general scholarly community as was expected by early digital theorists. The author attributes this failure to two main problems: a lack of easy-to-use tools and a lack of support from major publishing houses. The result is that it currently remains far easier to make a print than electronic edition. This situation will not improve until the tools and distribution of electronic projects is such that any scholar with the disciplinary skills to make an edition in print can be assured he or she will have access to the tools and distribution necessary to make it in the electronic medium.
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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 | 0.050 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.039 | 0.078 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".