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Record W1565999497 · doi:10.16995/dscn.37

Digital Humanities at Siberian Federal University

2015· article· en· W1565999497 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDigital Studies / Le champ numérique · 2015
Typearticle
Languageen
FieldComputer Science
TopicLibrary Science and Information
Canadian institutionsnot available
Fundersnot available
KeywordsDigital humanitiesScholarshipDigital scholarshipHumanitiesPromotion (chess)Library scienceIncentivePolitical scienceComputer scienceArtPoliticsLaw

Abstract

fetched live from OpenAlex

<p id="d1e62">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. <p id="d1e64"> 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.

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 score0.441
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0010.017
Open science0.0010.001
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 head0.043
GPT teacher head0.215
Teacher spread0.172 · 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