Co-learning for People, Places, and Planet
Bibliographic record
Abstract
How can a co-learning teaching methodology, drawing from human-centered design principles, utilise storytelling from around the world to create an environment of openness and inclusion within and beyond the classroom?Author Arianna Mazzeo draws on her personal experience designing and using co-learning methods with students from around the world, including Cape Town, Boston, Montreal, Barcelona, Dubai, Tokyo, and Tanzania. She examines how it can encourage learners to actively participate in shaping their educational experiences by leveraging the collective expertise and diverse perspectives of peers, mentors, and resources beyond the confines of a traditional classroom. Co-learning for People, Places, and Planet shows how this approach transcends age, background, and geographical boundaries, making education truly accessible to all, including non-traditional students.This book is ideal reading for teachers and students of Education and Education Studies, Human-Centered Design Research, Disability Studies, Cultural Anthropology, Design Learning Innovation, and Creative Arts, as well as engineers and community study leaders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".