School Library Trends: A Bibliometric and Content Analysis
Bibliographic record
Abstract
Today, AASL Twitter is one of the most widely used social media communications among school library practitioners. While scholarly communications in school library is conducted in an array of topics across the field of school library, it is difficult to establish how much of the scholarly communications is exposed to these practitioners. The study included three phases in its design: 1) To conduct a bibliometric analysis to find out the major authors, affiliations, themes and evolution of journals in field of school library, 2) To complete a content analysis of the AASL Twitter social media communications to find out the major participants, affiliations and themes and evolution in AASL Twitter communications, 3) To compare and contrast the major authors and themes of evolution in scholarly communications in the field of school library and AASL Twitter communications. During the years 1905-2018 scholarly communications have gone through various stages including infancy, growth and an upsurge stages. In recent years scholarly communications have been decreasing from the years 2010 to 2018. Trends in themes among scholarly communications and AASL Twitter communications include media, books, reading, Internet, children, literacy, standards, awards, technology, education, public, resources, teachers, students and electronic themes among other results. The trends between scholarly communications in the field of school library and AASL Twitter communications help provide support for future constructive goals among school library professionals.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.124 | 0.145 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".