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Record W7117689044 · doi:10.15802/unilib/2025_346270

Library and Information Services for National Minorities and Indigenous Peoples of Ukraine in the Context of Contemporary Challenges

2025· article· uk· W7117689044 on OpenAlexaboutno aff
I. O. PETROVA, YE. S. HORB

Bibliographic record

VenueUNIVERSITY LIBRARY AT A NEW STAGE OF SOCIAL COMMUNICATIONS DEVELOPMENT CONFERENCE PROCEEDINGS · 2025
Typearticle
Languageuk
FieldComputer Science
TopicLibrary Science and Information
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousContext (archaeology)Quarter (Canadian coin)National libraryEthnic groupUkrainian

Abstract

fetched live from OpenAlex

The objective of the proposed study is to highlight the issue of equal access to library and information services for national minorities and indigenous peoples of Ukraine in the context of countering the threats faced by the world in the first quarter of the 21st century. Methods. Methodologically, the article is based on the correlation of the concepts of power and culture, which in our case are represented by libraries and ethnic communities, respectively. Results. Based on the analysis of reporting materials and the content of the websites of Ukrainian library institutions, a number of successful approaches to the integration of national minorities and indigenous peoples into the library space were identified. However, in the vast majority of cases, there is a tendency towards declarativity and symptomatism in the implementation of IFLA guidelines aimed at achieving equality in the library and information sphere. The final part of the article and conclusions propose the development and implementation of a roadmap aimed at reducing the striking imbalance in access to library services for national minorities and indigenous peoples.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.222
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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