From Learning to Read to Reading to Learn: School Libraries, Literacy and Guided Inquiry
Bibliographic record
Abstract
School libraries are about the future. They are about the development of knowledgeable and knowing young people; young people who have the ability to read the word and the world, and who can live their lives as thinking, informed, knowledgeable and productive citizens of an increasingly inter-connected world. They are about young people who have the knowledge, skills, attitudes and values to invest wisely in confidently shaping their own futures and their lives as family, community and workplace members. Reading, Knowing and Doing are the multiple faces of the future global citizens that we nurture in our schools. Reading, Knowing, Doing, as the multiple faces of literacy, are the multiple faces of quality school libraries. And Reading, Knowing and Doing are at the heart of informed, in-tune, and in-touch school librarians committed to providing the best opportunities for our students to learn to use their minds well.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".