Experiences From the Field: Transforming Teaching and Learning Through Child Rights Education
Bibliographic record
Abstract
Children are not just the future; they are the present. Their voices must be heard and their rights fully implemented in the here and now. Empty promises for future actions do not build communities but destroy momentum. It is therefore essential that children learn about their rights, and for these rights to be at the core of teaching. Through reflective writing, I explore my personal and professional experiences as an educator, course lecturer, researcher and Child Rights Education (CRE) consultant in learning and teaching about the UN Convention on the Rights of the Child (CRC) while working in Canada and abroad for over a decade. Personal reflections from workshops, trainings and desk research have led to the understanding that it is essential for children to learn about their rights and for teachers to be trained in CRE to transfer this knowledge. This process of knowledge transfer can help educators and learners transform the CRC from a symbolic text to a living document, ensuring that child rights are lived (experienced) and living (contextualized and adapted to present and emerging needs), ultimately bridging diversities, leading to equitable practices and fostering understanding, respect and inclusion in and beyond the classroom walls. Informing the research findings are a conversation about child rights, an understanding of the constructed nature of childhood, and the role of creative drama as a pedagogical approach in transferring knowledge and opening the path for creative and collaborative practices and forms of inquiry in CRE.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.053 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.010 |
| 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".