Other Ways of Knowing: How School Librarians Can Take a Leadership Role in Addressing Multi-literacies Across the Curriculum in the School Library
Bibliographic record
Abstract
School librarians have a unique, unprecedented, and unparalleled opportunity to affirm their role in students’ use of basic literacy skills – reading and writing – while highlighting their relatively new role, guiding students through the acquisition of information through multiple modes of communication with new technologies. School librarians can create and facilitate opportunities for students to enhance their learning and become multiliterate. These learning opportunities and a focus on “core” literacies shed a much needed spotlight on the important role and influence of the school librarian on overall academic achievement and the acquisition of multiliteracy skills that have become a necessity in a changing technological and global environment. This article isbased on a presentation given at the International Association of School Librarians Conference in Doha, Qatar (2012), the goals of which were to share an overview of the multiliteracies concept, suggest strategies for facilitating literacy in the school library and classroom, and share professional resources for continued learning and the integration of multiliteracies across the curriculum.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.036 | 0.047 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".