Knowledge Management in Secondary Schools and the Role of the School Librarian
Bibliographic record
Abstract
At the beginning of the 21st century, educators are facing new visions, theories, and aims of education in a changing environment. In these "new kinds of learning," concepts such as individual learning, cooperative learning, e-learning, and ifelong learning have become important. New visions of learning suggest that pupils can learn to manage or control their own acquisition of knowledge. The introduction of ICT in schools has also contributed to these new visions and has caused enormous changes in the schools themselves. The role of the school librarian and the goals of the school library in the school have also changed. The school librarian needs to retain the ability to run and maintain the important traditional role of the school library, but must also act as an information specialist who coordinates the management of information and knowledge in the school and accesses information and knowledge from outside the school. As the new school information specialist, the school librarian may need retraining in knowledge management. Strong school management, strong infrastructure, and good communication in the school also are essential to the knowledge management process. The content of this article is part of the author's doctoral research.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".