Bibliographic record
Abstract
There were several epochs in history that have altered the life of mankind. The first epoch was when the oral text was written down. The second was when the German scribe Guttenberg invented the printing press, and the handwritten text became printed. Now text is becoming digital, and there is a natural digitalization of all spheres of human activity, including the legacy of Dostoevsky. Modern information technologies create a new type of text that not only preserves the advantages of oral, handwritten and printed text, but also acquires new capabilities. The digital text expands the range of sources, the volume of information, and stimulates new methods of studying the writer's creative work. Despite the fact that electronic libraries, which currently dominate the Internet, present digital copies of Dostoevsky's printed publications, new types of electronic publications and new tools for analyzing not only handwritten and printed, but also digital text, are emerging. The idea of Digital Dostoevsky is being implemented in Petrozavodsk University projects (since 1995), the Institute of Russian Literature of the Russian Academy of Sciences (since 2016), and the University of Toronto (since 2019). Lexicographic work on Dostoevsky's vocabulary is being carried out in digital format at the Russian Language Institute of the Russian Academy of Sciences. The article provides an overview and outlines the prospects for the development of Digital Dostoevsky. An important task of the global Digital Dostoevsky is the creation of national bibliographies and electronic libraries and publication of new sources related to the writer's life and work. It is necessary to create the conditions for optimizing and integrating the existing resources. The digital format allows to actively use new text analysis tools and information technology capabilities for research and educational purposes.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".