Incorporating information literacy in the Croatian school environment
Bibliographic record
Abstract
Despite the relevance, potential and impact of IL activities on the wider social and pedagogical context, libraries in Croatia are lagging far behind the emerging global information literacy movement. Due to the general lack of written and widely published educational programs, mission statements or policies of Information Literacy on a national level, the majority of school libraries in Croatia are forced to provide only simple and most fundamental forms oriented more towards library and not information literacy. Diagnosing the emerging problem and trying to find a solution to the problem presented an idea for a pilot project. Educating the educators i.e. school librarians was set as a starting point in implementing information literacy in the educational environment.
 Results of the pilot project carried out with the Education and Teacher Training Agency in advocating information literacy in the school environment are presented in this paper. Research on the theory of current education problems together with research on best case scenarios was used as a foundation for creating learning materials and organizing a series of workshops for educating school librarians. Future plans for expanding the project with all participants of the educational process (principals, teachers and school librarians) are discussed and key elements of possible information literacy program are defined.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".