Bibliographic record
Abstract
The urgency of the article is due to the increased impact of information and communication technologies on research in the field of socio-humanitarian cycle sciences, which led to the emergence of interdisciplinary direction of research and projects – Digital Humanities, increasing socially important digital content (electronic collections, archives, libraries, museums) and the need for training specialists for this area of activity. The author relies on the main provisions of the publications by V. Kopanieva, T. Yaroshenko, S. Chukanova, O. Oliinyk, and Manifesto for the Digital Humanities. The purpose of the paper is to analyze the experience of preparing specialists in Library Science and Information Sciences (LIS) in the interdisciplinary Master’s Degree Programs in Digital Humanities in Canada and determining the prospects for introducing such programs in Ukraine. The author uses the systematic approach to determining the prospects of training specialists in Digital Humanities for libraries, educational institutions, museums, archives, and other cultural institutions. Methods of analysis of literary sources and information resources, content analysis of university sites in Canada, comparative analysis of educational programs Digital Humanities are applied. The University of Alberta (Edmonton, Canada) was selected as the main research object, where the 3-year Interfaculty Educational Program upon Digital Humanities and the Master of Science in Library and Information Research (MLIS) is offered by the Digital Humanities Program and the Library for Information Studies School. The comparative analysis of the contents of the Interfaculty Educational Program upon Digital Humanities at the University of Alberta and the educational and professional program on Information, Library and Archives Management at Borys Grinchenko Kyiv University stimulated the decision to develop a new specialization in Digital Humanities for undergraduate Master’s program students in Ukraine. Digital Humanities combine information and communication methods and theories with research and teaching in the field of art and the humanities. The training of specialists in the field of DH is an actual and important task for the higher education system in Ukraine, and the experience of universities in Canada can be extremely useful.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".