The acquisition strategy of foreign-language books in public libraries: A comparative study
Bibliographic record
Abstract
Public libraries are facing a great challenge to succeed in serving their increasingly diverse communities. This thesis focuses on the acquisition of foreign-language books in public libraries and attempts to address the question whether libraries have special strategies to select and acquire foreign-language books. Particular attention is paid to fiction, the main genre offered by public libraries. To help answer the thesis question, the author compares the acquisition strategies of the Municipal Library of Prague and the Zentral- und Landesbibliothek Berlin using structured interviews with the key persons responsible for the acquisition of foreign-language books. As a background, the author provides a literature review of foreign-language materials in public libraries and specifically, the acquisition strategy for those materials (i.e. including case studies, research projects and trends from the Czech Republic, the US, Canada, Australia, Sweden, Denmark and Netherlands). This knowledge foundation is considered in the comparison of the two libraries and their acquisition strategies. The comparative study confirms the trends highlighted in the literature review, such as outsourcing, approval plans and gifts as common ways of acquiring foreign- language literature. This thesis further contributes...
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".