Reading Diversity through Canadian Picture Books: Preservice Teachers Explore Issues of Identity, Ideology, and Pedagogy (2013) Johnston, I., & Bainbridge, J. (Eds.)
Bibliographic record
Abstract
Picture books are a popular instructional resource, and few individuals would challenge their common recognition as useful classroom tools.Ingrid Johnston and Joyce Bainbridge, however, aim to replace the prevailing notion that illustrated volumes are best suited for young learners with a broader appreciation for such books' instructional use and value.In support of their goal, a multi-site national research study was deployed to investigate connections between picture books and "preservice teachers' sense of national identity and their perceptions of the diverse needs of Canadian students" (p.3).Their edited book is a collection of chapters that shed insight on teacher candidates' perceptions of topics that range from historical wrongs, such as how colonization harmed Indigenous communities, to if and how social justice orientations become actionable in classroom instruction, such as by engaging in reflective discussions with students.Reading Diversity through Canadian Picture Books: Preservice Teachers Explore Issues of Identity, Ideology, and Pedagogy opens with an introduction that provides an overview of the overarching purpose, procedures, theoretical framework, and objectives for the project.At first glance, the book appears to provide additional backing for the frequently reported contention that many teachers do not readily or critically engage diversity themes in classroom instruction.A more thoughtful consideration of the text, however, highlights a worthy journey that guides readers through trends that are too often underexplored in teacher education research.Chapter 1 by Joyce Bainbridge and Beverley Brenna centered on participants' understandings of critical literacy and their sense of social responsibility as teacher candidates and novice educators.Based on data that they collected at the University of Alberta, in Edmonton, Canada, Bainbridge and Brenna concluded that the teacher candidates with whom they worked generally favoured social justice principles.Yet, subsequent extensions to the classroom appeared to be limited as participants expressed reservations that were linked to their respective teaching contexts.Teacher candidates who planned to teach in elementary schools voiced concerns about broaching controversial subjects while individuals with secondary placements seemed reluctant to accept picture books as age-appropriate materials.Ingrid Johnston and Farha Shariff further explored the study participants' beliefs about classroom practice in Chapter 2. The authors enlarged the discourse on teacher perceptions by "considering how … texts challenged participants' sense of self and taken-for-granted views
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".