Reconceptualizing Early Childhood Educators as Champions for Children’s Rights: Introduction of the Children's Rights Advancement Framework
Bibliographic record
Abstract
This article aims to further the representation of children's rights in literature and practice of Early Childhood Education (ECE) and introduces the Children's Rights Advancement Framework (CRAF). Despite theoretical alignment between the United Nations Convention on the Rights of the Child (UNCRC) and ECE policies, practical implementation and actualization remains elusive. During a conference presentation the authors of the article discovered through an anonymous survey that nearly 80% of the attendees, Early Childhood Educators, had little knowledge of UNCRC and its articles. Inspired by the work of the late Landon Pearson, an advocate for the rights of children, this paper critically reviews existing literature, identifies barriers to implementation in the Early Learning field, and introduces the CRAF as a transformative model. Drawing on a rights-based approach, the CRAF includes stages of conceptualization, implementation, application, and actualization of children's rights. The article emphasizes the need for awareness, education, and scaffolding knowledge of rights for both educators and children. The article highlights the transformative potential of the CRAF and offers practical implications and policy recommendations for advancing children's rights in ECE.
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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".