International Symposium on the Future of Digital Editing and Publishing: Selected Presentations
Bibliographic record
Abstract
Selected presentations from the International Symposium on the Future of Digital Editing & Publishing, hosted at University College Cork in June 2024. The International Symposium on the Future of Digital Editing & Publishing was a conference dedicated to exploring the state-of-the-art in digital scholarly editing and publishing. Topics included the evolution and future trajectories of digital scholarly editing and publishing; digital textual scholarship and innovative approaches to editing and publishing; the role of artificial intelligence in the editing and publishing process; the implications of born-digital cultural materials for digital scholarly editing and publishing; open access, copyright, sustainability, and the economics of digital publishing; digital pedagogy and the incorporation of scholarly editions in academic curricula. T The International Symposium on the Future of Digital Editing & Publishing was organised by C21 Editions, an international collaboration between University College Cork, the Digital Humanities Institute at the University of Sheffield, and the University of Glasgow. C21 Editions was funded by the Irish Research Council and UKRI-AHRC under the UK-Ireland Collaboration in the Digital Humanities Research Grants, grant numbers AH/W001489/1 and IRC/W001489/1.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.026 |
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".