Bibliographic record
Abstract
Making School with Children: Engaging Learners to Think Critically envisions a hopeful future where communities unite to support one another and create a better world. Inspired by Comber’s (2016) reminder that young people should learn to work for justice from the outset of schooling, this book showcases a whole-school response. Readers will explore stories from K-6 classrooms, where students and teachers collaborated across grade levels to build critical literacy through civic-minded social action projects, driven by the children’s inquiry questions, interests, and passions. Making School with Children reflects Vasquez’s (1994, 2014) belief that learners thrive when their education is relevant to their lives. By centering children’s experiences, the book aims to foster what Routman (2023) describes as a “joyful culture of trusting relationships, respect, and celebration of learners’ strengths.” This approach not only enhances curriculum but also nurtures a dynamic relationship with the world, promoting change, progress, and the creation of new ideas. The book is divided into two sections. The first section, comprising seven chapters, details the work done in each grade from kindergarten to sixth grade to negotiate civic-minded literacy learning that transcends mandated curricula. It concludes with lessons learned and their implications for other educational settings. The second section offers a collection of resources, including recommended children’s books, teacher resources, teaching tips, and lesson plans, to support similar work in the readers’ own settings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".