A <i>Significant Events Approach to Children’s Rights</i> with children under three in early childhood education and care
Bibliographic record
Abstract
The UN Convention on the Rights of the Child is one of the most widely-ratified human rights treaties, yet the visibility of children in the early years, in the mandatory government reports to the UN Committee on the Rights of the Child and in the Committee’s concluding observations to States Parties, is relatively low (Lundy, 2020, “Implementing the Rights of Young Children: An Assessment of the Impact of General Comment No. 7 on Law and Policy on a Global Scale,” In Routledge International Handbook of Young Children’s Rights, edited by J. Murray, B. B. Swadener, and K. Smith, 15–29, London: Routledge). This paper sets out an innovative approach to record and analyse rights-based practice with young children, which focuses on significant events in a child’s daily life that could help raise the visibility of children in the early years as rights holders. The analysis of 75 h of participant observations across two settings in England and two in Finland, involving 16 two-year-old children, revealed that there were five rights that cut across the two countries and related to all children. The study found that having a way of recording rights-based practice is important if we wish to support all children, in learning about, experiencing, and exercising their rights, so that they can have an impact on pedagogical practice and thus influence their own lives in ECEC.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".