Bibliographic record
Abstract
School libraries exist in most countries where they play an important role in education. However, the diversity of educational philosophies, policies, resources, infrastructure and technology have led to wide variations in what constitutes a ‘school library’, and its associated terminology. This diversity affects communication, collaboration, synthesis of evidence, and translation of research findings. If we are to promote school librarianship internationally, it is important to have a common terminology. This paper presents the preliminary findings of an international survey on the terminology and characteristics of school libraries, Based on responses from school library stakeholders from 19 countries, it was found that the more common term used to describe the facility was ‘school library’, while term used for the person managing it was ‘school librarian’ or ‘teacher librarian’. While few countries had a formal definition of a school library, the respondents were able to provide the desired characteristics of a school library and of a teacher-librarian. These findings can be used to develop consistent and internationally-applicable terminology and definitions for school librarianship.
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 | 0.166 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.039 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.036 | 0.044 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".