Advancing early childhood development for children with disabilities and the Global Disability Summit 2025
Bibliographic record
Abstract
In 2015, the global health community, represented by 193 UN Member States, committed to ensuring inclusive, high-quality education for children with disabilities under the Sustainable Development Goals (SDGs), 2015–2030. The SDGs also recognised optimal early childhood development (ECD) as a critical pathway to disability-inclusive education. It was, therefore, imperative to have a robust and globally coordinated disability-focused ECD initiative—one that prioritises early detection and intervention services from birth to 5 years of age—to ensure school readiness and lifelong inclusion for children with developmental delays and disabilities.1–3 UNICEF was rightly designated under the SDGs as the lead custodian UN agency to advance this global vision for children with disabilities. In pursuit of the mantra ‘nothing about us, without us’, the Global Disability Summit (GDS) was launched in 2017 to promote disability inclusion across health, education, social and economic sectors as a fundamental human right (https://www.globaldisabilitysummit.org/). Since its inception, three summits have taken place: in 2018, 2022 and most recently in April 2025. This article examines the progress on ECD by UNICEF for children with disabilities against the backdrop of GDS 2025 and SDGs.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".