The Open Source School Library Research Database: Learning without Borders
Bibliographic record
Abstract
This paper introduces the Open Source School Library Research Database (OSSLRD) and highlights the 940 items in the database. Clyde’s checklist (2001) was used to identify articles, conference papers, dissertations and theses that are included in the OSSLRD. Each identified piece of research was examined and key information was gathered including author(s), date of publication, journal, participants, method(s), location of the study (if available), research methods, themes, and findings. Similar trends from previous research were confirmed: research in school librarianship is published in two major journals: School Library (Media) Research and School Libraries Worldwide. There are a small core group of researchers working in the area of school librarianship. More than half of all research is by a single author.
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 | 0.041 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.043 | 0.064 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.116 | 0.113 |
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".