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Record W3005565006 · doi:10.1080/00085006.2019.1708530

Printed and digital wor(l)ds: retrospectives and perspectives of scholarly editing in Slavic countries

2020· article· en· W3005565006 on OpenAlexvenueno aff
Dmytro Yesypenko

Bibliographic record

VenueCanadian Slavonic Papers · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsSlavic languagesAmateurUkrainianContext (archaeology)Slavic studiesPublishingSlovakSociologyHistoryCzechMedia studiesLiteratureLinguisticsClassicsArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.197
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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