The Ukrainian Kyrylytsia, Restored: An Automation Project for Adding the Cyrillic Fields to Ukrainian Records in OCLC WorldCat
Bibliographic record
Abstract
This report from the field concerns a collaborative project which resulted in successfully adding the Cyrillic fields to about 30,000 Ukrainian bibliographic records in OCLC WorldCat, the world’s largest online catalogue. Historically, the Ukrainian records in English-speaking libraries were only provided in transliteration according to the Library of Congress Romanization Table. However, the current standards also require the original script, such as the Ukrainian Kyrylytsia. While automating the Cyrillicization of Ukrainian legacy records is theoretically straightforward, in practice it faced more than one challenge, from poor quality of transliteration to the historical changes in Ukrainian orthography. The report presents the OCLC Ukrainian Cyrillicization project and discusses the steps in its implementation as an example of a successful collaboration in the areas of bibliographic automation, Ukrainian philology and culture, Slavic cataloguing, and linguistics.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".