Race and the Prizing of Children’s Literature in Canada: Spotlighting Canada’s Governor General’s Literary Awards
Bibliographic record
Abstract
Schooled in the professions of librarianship and teaching, I have been socialized to pay attention to, and to validate the prizing of children’s literature. In the discussion that follows, I employ critical race theory (CRT) (Aylward 1999; Delgado and Stefancic 2000; Gillborn 2005, 2006; Goldberg 2009; Ladson-Billings 2005, Ladson-Billings and Tate 1995) and post-colonial thought (Battiste 2009; Yazzie 2009; Bradford 2007) to critically examine how racialization in the context of colonial/post-colonial domination and subordination has inuenced the prizing of children’s literature in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.037 | 0.026 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".