Bibliographic record
Abstract
Research in digital libraries (DLs) has gained much interest across the globe. Most funding related to DL are available for building DLs, rather than producing digital librarians by developing the DL curricula and offering necessary funding to introduce state-of-the-art DL labs for future library professionals. Based on online surveys, this article investigates the status of DL education/courses in Europe, particularly, it examines the curriculum contents of DL courses, explores the future direction of library and information science (LIS) curricula, and identifies the competitors of LIS schools in the DL world. This study received responses from 54 LIS schools/departments in 27 European countries. The results of the current study clearly show that the majority of the LIS schools have already integrated digital librarianship in their regular bachelor’s and master’s degree programs. The importance of practical aspects in DL curricula has been highlighted by the authors. The study also reports the recommended books and journals on DL, direction of LIS curricula, and the competitors of LIS schools in the digital world. A number of future research directions have been offered by the authors. The authors expect that the study will contribute to the discussions and debates toward identifying subject elements for DL courses. The top subject areas based on their importance as reported by the participants of the current study should be taken into consideration before designing curricula for DLs and before developing a Europe-wide unique LIS curriculum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".