Exploring Research Topics in the Field of School Librarianship based on Text Mining
Bibliographic record
Abstract
This study used text mining to explore research topics in the two leading research journals in the field of school librarianship, School Libraries Worldwide and School Library Research. Titles and abstracts were collected from 225 articles of the two journals for the 10 years, 2006 through 2015. Term frequency analysis and topic modeling based on Latent Dirichlet allocation were employed to analyze the collected data. The findings showed the most frequently observed terms and imply the importance of learning, education and programing in school library research. Topic modeling extracted 20 research topics in the field including: school library programming; information literacy; professional roles; digital and technology leadership; research design; policy and management; and others. This study confirmed that programming related topics have been the most widely researched in school librarianship. In both journals, programming is a popular topic. Additionally, professional role, technology, and inquiry skills are amongst popular topics in School Libraries Worldwide, while information literacy, reading, and learning are more common topics in School Library Research.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.056 | 0.045 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".