The key to digitization success: making the school librarian a true pedagogical partner
Bibliographic record
Abstract
In Linköping there is an ongoing investment to digitize schools. This means that schools are equipped with projectors, computers, iPads and Apple TV for wireless transmission to the whiteboard. The investment will hopefully lead to a pedagogical improvement in teaching and higher achievement among pupils. Many teachers need more knowledge to give pupils good opportunities for active learning with digital tools. In order to meet teachers' needs for skill development, Linköping has together technology investments also made a major investment in school libraries with employed librarians, so called focus libraries. All schools with focus libraries have educated school librarians with full-time mission integrated in the school, working together with teachers. The librarian devotes most of her time in the classroom with the teachers to plan, implement and evaluate the area of work and assess the pupils’ efforts. In this cooperation digital tools is often used.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.037 | 0.035 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".