Easy-to-Read Book Material in Croatian Public Libraries
Bibliographic record
Abstract
The paper presents findings of a study that evaluated the inclusion of easy-to-read materials in Croatian public libraries. Using a checklist method, catalogues of the 41 largest and smallest public libraries in each Croatian county were compared with the Bibliography of Printed Easy-to-Read Materials Published in Croatia, comprising 29 titles. The collected data were analyzed descriptively using SPSS. Findings show that only one studied library holds all titles, and nearly half of them have more than half of the titles listed in the Bibliography. Although there are some deviations, the study identified the following pattern: an increasing number of titles and copies was observed relative to county population size and library type by service area. In most cases, the largest number of copies and titles is found in the largest libraries. As the study focused only on the largest and smallest county libraries, the results are not generalizable to all Croatian public libraries. The results highlight the strengths and weaknesses of the surveyed collections regarding their coverage of easy-to-read materials and offer guidance for librarians in future collection development of this material. Given that reading difficulties, especially dyslexia, are issues that can significantly impair one’s quality of life, public libraries have the opportunity, through the acquisition and promotion of easy-to-read materials, to substantially contribute to improving the quality of life for such individuals and to raise public awareness of these issues. This is the first research that investigated the inclusion of easy-to-read materials in the collections of public libraries in Croatia.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".