Printed and digital wor(l)ds: retrospectives and perspectives of scholarly editing in Slavic countries
Bibliographic record
Abstract
This article raises the question of the continuity of national traditions of scholarly editing (from print to digital), and points to the possibility of overcoming the “inertia of tradition.” It first considers the transition of the editing and publishing of literature in Slavic countries from amateur activities based on subjective principles to scholarly editing. The author pays particular attention to the evolution of the editor’s role, as well as to opportunities for researchers, editors, and publishers in the context of digitizing the humanities. The second part of this article focuses on pioneering attempts at digital representations of Slavic literatures, their problems, and achievements. The author concludes with some observations concerning the role played by editions of authors regarded as classics in the evolution of national identities in Slavic countries. He argues that significant achievements in print editing do not guarantee success in digital editing, nor do relatively modest achievements and limited possibilities in print editing preclude success in the digital representations of national literatures. Most examples, observations, and generalizations refer to the history of scholarly editing of Polish, Russian, and Ukrainian literatures. However, speaking about contemporary editing, the author also addresses the experience of scholars from Czech, Slovak, Slovenian, and Anglo-American academia.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".