First Nations children’s books in a public library context – considerations for sharing
Bibliographic record
Abstract
First Nations children’s books should be read and enjoyed, and should be promoted and made accessible by library professionals. However, hoping that all library professionals will just ‘get out there’ and work with First Nations children’s books, risking criticism and disapprobation, may be unrealistic – and here an addition to current practice could help. Crystallizing corrective input is demanding, painful though rewarding, and subtle – it is not something that happens easily, without effort and thought. Appropriately-led workshops designed to help librarians inform themselves more fully about First Nations children’s books, and to develop good practices around how to share them, would garner real benefits in effective, confident promotion and sharing of these valuable collection materials.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".