University Preparation Programs for School Librarians
Bibliographic record
Abstract
This three-hour workshop provides an opportunity for school librarianship professors to discuss and share with their peers best practices in creating relevant assignments for school librarians in the 21st century on the topics of collaboration and leadership. For each topic, the professors from Australia, Canada, and the United States will share the following on a large screen and with handouts: (a) national standards that guide course preparation, (b) the course description, (c) course objectives, and (d) one sample assignment. Assignments for both topics will include the instructions and also the rubrics. After each topic presentation, participants are encouraged to share how they teach collaboration and leadership, and they will then be divided into small groups to share additional ideas. A third component of the presentation focuses on a 2013 U.S. grant from the Institute of Museum and Library Studies to deliver four online courses for doctoral candidates from various institutions with an interest in school library doctoral studies. The session will close with the participants brainstorming critical issues and topics for future IASL presentations from school librarianship professors. Before the IASL conference begins, emails will be sent to attendees who are school library professors to encourage them to attend and to bring sample assignments on teaching collaboration and leadership as a way to extend our conversation beyond the Australia, Canada and the United States.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.439 | 0.275 |
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".