Teaching Canadian Children’s Literature: Learning to Know More
Bibliographic record
Abstract
Dans cet article, les auteurs examinent les problèmes reliés à l'enseignement de la littérature canadienne pour la jeunesse au niveau universitaire. Or cette production littéraire est-elle avant tout de la littérature pour la jeunesse au de la littérature canadienne? En se concentrant sur des questions de genre et de spécifité nationale, les auteurs et leurs étudiants ont développé une typologie des caractéristiques de la littérature pour la jeunesse au Canada anglais. P. Nodelman et M. Reimer analysent les activités scolaires proposées en termes de pédagogie et d'exploration de la canadianité. \n \n This article explores the problems and the excitements of teaching Canadian children's literature in a university context. Is this literature most significantly children's literature or Canadian literature? By focusing simultaneously on both generic and national paradigms, the authors and their classes developed a provisional list of the characteristics of mainstream Canadian children's literature. The article explores the implication of these classroom activities in terms of both pedagogy and the exploration of the Canadian features of Canadian literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".