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Record W3108577915 · doi:10.47315/archives2019.318.087

The Experience of Ukrainian Archives in the Creation of the Digital Access Fund of Documents of the National Archival Holdings

2019· article· en· W3108577915 on OpenAlexaboutno aff
Liudmyla Didukh, Наталія Залєток

Bibliographic record

VenueArchivi Ukraїni · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsDigitizationUploadDocumentationLibrary scienceSpecial collectionsDigital libraryComputer scienceWorld Wide WebUkrainianBusinessTelecommunications

Abstract

fetched live from OpenAlex

On the basis of synthesis of materials of the study conducted by the Ukrainian Research Institute of Archival Affairs and Records Keeping in 2018, information on the common practices of the Ukrainian archives in creation of the digital access fund was provided. The main problems, the solution of which can signifi cantly optimize organization of work of the digital access fund, was singled out. References: CIMS-S001. Information Management Standard – Creating and Managing Digitized Records. (2015). Toronto. Retrieved from: https://www.toronto.ca/ wp-content/uploads/2017/08/971f-Creating-and-Managing-Digitized-Records-pdf. [in English]. Įsakymas dėl skaitmeninio turinio kūrimo ir valdymo valstybės archyvuose tvarkos aprašo patvirtinimo 2018 m. lapkričio 26 d. Nr. VE-90. Retrieved from: https://e- seimas.lrs.lt/portal/legalAct/lt/TAD/28200462f1bd11e89d4ad92e8434e309?jfwid=1 v569an2a [in Lithuanian]. ISO/TR 13028:2010. Information and documentation – Implementation guidelines for digitization of records. Retrieved from: https://www.iso.org/standard/52391. html [in English]. Puglia, , Reed, J. & Rhodes, E. (2004). Technical Guidelines for Digitizing Archival Materials for Electronic Access: Creation of Production Master Files. Raster Images. Retrieved from: https://www.archives.gov/files/preservation/technical/guidelines.pdf [in English]. Banach, M., Shelburne, B., Shepherd, K. & Rubenstein, A. (2011). UMass Amherst Libraries Guidelines for Digitization: Retrieved from: https://www.library.umass.edu/assets/Digital-Strategies-Group/Guidelines-Policies/ UMass-Amherst-Libraries-Best-Practice-Guidelines-for-Digitization-20110523- templated.pdf [in English]. Zarządzenie Nr 14 z 31 sierpnia 2015 r. w sprawie digitalizacji zasobu archiwalnego archiwów państwowych. Retrieved from: https://archiwa.gov.pl/images/docs/ akty_normatywne/zarz_14-pdf [in Polish]. Didukh, L. V. (Comp.) (2016). Kopiiuvannia dokumentiv u arkhivnykh ustanovakh Ukrainy: metodychni rekomendatsii. Derzhavna arkhivna sluzhba Ukrainy, UNDIASD. Kyiv. Retrieved from: http://undiasd.archives.gov.ua/doc/mr_copy_docs.pdf [in Ukrainian]. Metodicheskie rekomendacii po podgotovke i peredache arkhivnyh dokumentov dlya ocifrovyvaniia, ucheta i khraneniia cifrovyh kopij: utv. prikazom direktora Departamenta po arkhivam i deloproizvodstvu Ministerstva yusticii Respubliki Belarus ot 25.11.2008 № 38. Retrieved from: https://archives.gov. by/index.php?id=133837#mr [in Russian]. Prykhodko, L. V. (Comp.) (2014). Oblik dokumentiv u derzhavnykh arkhivakh Ukrainy: instruktsiia. Derzhavna arkhivna sluzhba Ukrainy, UNDIASD. Kyiv. [in Ukrainian]. Pravyla roboty arkhivnykh ustanov Ukrainy, zatverdzheni nakazom Ministerstva iustytsii Ukrainy vid 08 kvitnia 2013 r. № 656/5, zareiestrovani u Ministerstvi yustytsii Ukrainy 10 kvitnia 2013 r. za № 584/23116 (zi zminamy). Retrieved from: https://zakon.rada.gov.ua/laws/show/z0584-13 [in Ukrainian]. Khartiia o sokhranenii cifrovogo naslediia. In Akty Generalnoi konferencii, 32 sessiia Parizh, 29 sentiabria – 17 oktiabria 2003 g. T 1. Rezoliucii. Retrieved from: http://unesdoc.unesco.org/images/0013/001331/133171r.pdf [in Russian]. Didukh, V., Zalietok, N. V. & Kovtaniuk, T. M. (Comp.) (2018). Tsyfrovyi fond korystuvannia dokumentamy Natsionalnoho arkhivnoho fondu: stvorennia, zberihannia, oblik ta dostup do noho: metodychni rekomendatsii. Derzhavna arkhivna sluzhba Ukrainy, Ukrainskyi naukovo-doslidnyi instytut arkhivnoi spravy ta dokumentoznavstva. Kyiv. [in Ukrainian].

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.539

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 communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.263
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2019
Admission routes1
Has abstractyes

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