Introducing the Digital Scholarly Editions Initial Training Network (DiXiT ITN), 2014-2017
Bibliographic record
Abstract
The Digital Scholarly Editions Initial Training Network (DiXiT ITN) is a Marie Skłodowska-Curie Action (MSCA) underwritten by the EU through the 7th Framework People's Programme. It is an international collaboration among ten partner institutions and several associated partners from the public and private sectors, all actively involved in the creation and publication of digital textual scholarship projects, and specifically digital scholarly editions. The core of the DiXiT project is a set of coordinate training and research events for early stage (PhD) and experienced (postdoctoral) researchers. These events provide training in the multidisciplinary skills, technologies, theories and methods of digital scholarly editing. The network also prioritizes offering DiXiT fellows chances to improve transferable skills that can be used across sectors, such as project management, event organization, financial management, grant writing, language acquisition, and strong written and presentation skills. As a group of DiXiT Fellows, this poster introduces the topics of our projects as well as our plans for the next three years.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.020 |
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".