Bibliographic record
Abstract
A CHOICE Outstanding Academic Title 2012! Based on case studies from public schools in Toronto, Canada, this book chronicles an inspiring five-year journey to develop thinking about and teaching literacy for the 21st century. The research, which was classroom-based and developed by public school teachers in collaboration with university researchers, was stimulated by an ethnographic study at Joyce Public School to track children learning to read in an era of multiliteracies. Following the kindergarteners’ interest in Goldilocks and the Three Bears, Lotherington asked the principal: What would Goldilocks look like, retold through the eyes of the children? The resulting classroom experiment to transform learning to read a storybook into multimodal collaborative story-telling sparked the development of an award-winning school-university learning community dedicated to the development of multimodal literacies in the culturally diverse, urban classroom. Pedagogy of Multiliteracies tells the evolving story of teachers’ trial-and-error interventions to engage children in multiple modes of expression involving structured play with contemporary media. Using the complex texts created, the teachers carve spaces to welcome the voices of children and the languages of the community into the English-medium classroom.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".