Bibliographic record
Abstract
This article presents arguments to support social inclusion as an important function of the school library. Unlike other types of libraries, the school library is not a separate organization, but lives within the school, which has social inclusion as its mission. In order for the school library to fulfill a social inclusion role in cooperation with the school, it must look at the changes that are underway in education systems because of the process of globalization and digitalization of information. Two changes in particular— the movement from the transmission of knowledge to the formation of key competences or capabilities and the growing weight of international evaluations of educational systems—are effecting a paradigm change for schools and school libraries: from the axis of having information knowledge to the axis of being and becoming competent and capable. The school library has the potential to be an essential driver of social inclusion and educational innovation by playing a unique educational role in two areas: the logical organization of information, which can help to address scholastic dispersion (the dropout problem), and the documentation of professional knowledge to help the school become a learning organization (knowledge management).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".