Bibliographic record
Abstract
Purpose – This paper aims to investigate the quality of access to translated fiction published between 2007 and 2011 in six large Canadian public libraries, answering the question about what public libraries can do to help acquaint their readers with international translated fiction. Design/methodology/approach – The article uses the method of bibliographic data analysis based on 2,100 catalog records. Findings – As the results demonstrate, enhanced bibliographic catalog records deliver a wealth of information about translated fiction titles and facilitate meaningful subject access to their contents. At the same time, promotional activities related to translated fiction have room for improvement. Practical implications – Despite the fact that the study focuses on public libraries, its findings will be of interest not only to public but also academic librarians, any librarian tasked with the selection and acquisition of translated fiction, reference and readers’ advisory librarians in any type of library, Library and Information Science students and anyone interested in access to translated fiction. Originality/value – While many recent studies have turned their attention to enhanced catalog records and their role in access, discovery and collection promotion, there are no studies dealing with translated fiction specifically. The article also contributes to seeing an in-depth understanding of bibliographic records and cataloging as part and parcel of reference librarians’ knowledge and skill set, which improves retrieval practices and access provision.
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 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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.045 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".