Learning about translators from library catalog records: implications for readers’ advisory
Bibliographic record
Abstract
Purpose – The purpose of this article is to make public librarians aware of the wealth of information about translators that is contained in bibliographic records of their own library catalogs so they could use this information for the benefit of readers’ advisory (RA) work involving translated titles. Design/methodology/approach – The article uses the method of bibliographic data analysis based on 350 selected translated fiction titles (and 2,100 corresponding catalog records) from six large Canadian public libraries. Findings – As the results demonstrate, enhanced bibliographic catalog records deliver a wide spectrum of information about translators, which can be used by public libraries to provide more informed and insightful reading advice and to make more sensible purchasing decisions with regard to translated fiction. Practical implications – The study shows how the most readily available tool – a library catalog with its enhanced bibliographic records – can be utilized by public librarians for improving RA practices. It focuses on the rarely discussed translated fiction, demonstrates a sample methodological approach and makes suggestions for implementing this approach by busy public librarians in real-life situations. Originality/value – No recent studies that have investigated enhanced catalog records have dealt with translated fiction. Moreover, while authors/writers are often in the focus of RA studies, translators are often left behind the scenes, despite their crucial role in bringing international fiction to English-speaking readers.
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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 | 0.113 | 0.474 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".