Digital Humanities at Siberian Federal University
Bibliographic record
Abstract
The paper explores digital scholarship at Siberian Federal University and considers the incentives for Siberian scholars to use digital data in their research, build their own databases and digital editions, and develop research questions based on new methods and tools. Although the first stage of digital scholarship (providing digital content) seems to be a well mastered skill for Siberian researchers, the second stage of working with digital data and new research questions in the humanities does not seem to be within their comfort zone. We conclude that random digital humanities initiatives do not guarantee either demand for digital humanities or knowledge and understanding of new research questions inspired by new methodologies. Further studies might be needed to understand if we require lobbying for digital humanities and, if so, what kind of promotion, dissemination and training are needed or would be most effective. L'article explore les travaux d'érudition en version numérique à l'université fédérale de la Sibérie et considère ce qui pourrait motiver les universitaires sibériens à utiliser les données numériques dans leurs recherches, à bâtir leurs propres bases de données et éditions numériques, et à élaborer des questions de recherche basées sur les nouvelles méthodes et nouveaux outils. Bien que les chercheurs sibériens semblent avoir bien maîtrisé la première étape des travaux numériques (fournir du contenu en version numérique), ils ne semblent pas être dans leur zone de confort pour ce qui est de la deuxième étape, soit travailler avec les données numériques et les nouvelles questions de recherche en sciences humaines. Nous concluons que les initiatives aléatoires de sciences humaines numériques ne garantissent pas qu'il y aura une demande en sciences numériques, ni des connaissances et une compréhension des nouvelles questions de recherche inspirées par les nouvelles méthodologies. Il peut être nécessaire de mener d'autres études pour comprendre si nous devons faire des pressions en faveur des sciences humaines numériques, et dans ce cas, quel est le type de promotion, de diffusion et de formation qui serait nécessaire ou qui pourrait être le plus efficace.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.010 |
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".