The challenges of a school library in the digital age as a resource helping educating refugee children and integrating them into the Swedish society
Bibliographic record
Abstract
Approximately 163 000 refugees came to Sweden in 2015, mainly from Afghanistan, Iraq, Syria and Somalia. Many of them were just children. These children have faced extreme dangers and endured extreme hardships but once in Sweden they receive a school education. A number of these children have been designated to schools in Spånga, in Stockholm, the capital of Sweden where I work. The absence of parents, language barriers, cultural differences, lack of earlier education along with other challenges must be acknowledged and addressed constructively in order for the school library to make a difference. A school library built for the digital age might give access to thousands of books, modern information technology, Internet and digital resources but it requires of its ́ users to be literate and to have digital literacy. Many of the children's language- and information and communication technology skills are poor and they are therefore not able to make good use of the school library without help. It requires of the professional school librarian to bridge over the gap.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".