“No text is innocent”: Canadian children’s books in the classroom
Bibliographic record
Abstract
This case study of one school district explored elementary teachers’ use of, and beliefs about, Canadian children’s books in the classroom. It also examined the supports that facilitate elementary teachers’ use of Canadian books, including services provided by teacher-librarians, school district personnel and others; availability of funding; and opportunities for professional development. The case study district had a teacher-librarian in every school and was committed to supporting the work of teachers through the school library. The participants had clear ideas about what it meant to them to be Canadian and about the values that were important to them as Canadians. They believed it was important to incorporate Canadian books into classroom activities. However, they often felt a need to justify their use of Canadian books, whereas they unquestioningly used American books in their teaching across the curriculum. Overall, they were more knowledgeable about Canadian books and Canadian authors and illustrators than teachers in earlier studies. However, the findings of the study raise an interesting paradox; the teachers supported the use of Canadian books but they did not appear to connect “Canadian values” to Canadian books. They seemed unaware that all books, Canadian or not, convey an ideology; “no text is innocent” (Stephens & Watson, 1994, p. 14).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.041 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".