Dealing with False Friends to Avoid Errors in Subject Analysis in Slavic Cataloging: An Overview of Resources and Strategies
Bibliographic record
Abstract
This article discusses errors in subject analysis for Slavic materials due to “false friends,” words similar in form but different in meaning. An overview of false friends in Slavic languages is presented, followed by case studies demonstrating how false friends in book titles can lead to errors in subject analysis. We also review the resources that can assist Slavic catalogers in identifying false friends: print dictionaries, online lists, online translation tools, and records from the catalogs by the national libraries of countries of publication. Finally, implications for workflow in dealing with Slavic false friends in cataloging practice are discussed.
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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 | 0.052 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.029 | 0.015 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".