Building a Vibrant Future for School Librarians through Online Conversations for Professional Development
Bibliographic record
Abstract
Technology and social media platforms are driving an unprecedented reorganization of the learning environment in and beyond schools around the world. Technology provides us leadership challenges, and at the same time offers opportunities for communication and learning through technology channels to support professional development. School librarians and teacher librarians are often working as the sole information practitioner in their school, and need to stay in touch with others beyond their own school to develop their personal professional capacity to lead within their school. The Australian Teacher Librarian Network aims to make a difference, and supports school library staff in Australia and around the world to build professional networks and personal learning connections, offering an open and free exchange of ideas, strategies and resources to build collegiality. This ongoing professional conversation through online and social media channels is an important way to connect, communicate and collaborate in building a vibrant future for school librarians.
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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.033 | 0.043 |
| Open science | 0.002 | 0.042 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".