CLARIN Workshop at RANLP 2025 - The First Workshop on Natural Language Processing and Language Models for Digital Humanities (LM4DH 2025)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This workshop aimed to create a collaborative platform for researchers, practitioners, and students from various fields to explore the application of AI-driven techniques in the Digital Humanities. Through interdisciplinary dialogue and knowledge exchange, the event sought to foster innovative methodologies, promote best practices, and cultivate a research community dedicated to advancing computational approaches to human culture, memory, and history. Members of the CLARIN network organised the workshop as part of the Recent Advances in Natural Language Processing (RANLP) 2025 conference, which took place from September 8 to 20, 2025, in Varna, Bulgaria. Conference and workshop organisers contribute to the CLARIN K-centre for Large Language Models in SS&H (LLMs4SSH). For more info, see the event page on the CLARIN website.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it