An urban neighbourhood framework for realising progress towards the New Urban Agenda for equitable early childhood development
Bibliographic record
Abstract
Children consistently exposed to positive, stimulating environments, including their spatial neighbourhood, experience foundations for ongoing optimal development. Recognition of the neighbourhood as a mechanism to enhance wellbeing is reflected in the United Nation's New Urban Agenda. Through a series of innovations and advancements, this paper aims to describe a spatial indicators' framework, the Neighbourhood Early Childhood Development Framework. Once tested, it can be used to assess and monitor urban neighbourhoods for their ability to equitably support early childhood development. Eight domains were included in the framework: Early childcare and education services, family-friendly destinations, food outlets, housing, public open space, public transport, traffic, and walkability. Overall, 44 indicators were conceptualised for calculation at the smallest appropriate geographical scale available (i.e. a child's home address). Different scales of analysis were chosen to represent a child's local neighbourhood in an urban setting. The Framework supports commitment to action in the New Urban Agenda through improved measurement, monitoring, and research capabilities, alongside provision of tools to support evidence-based and interdisciplinary policy and practitioner decision-making. Once tested with child outcomes, it can inform more precise, evidence-based place-based interventions, while offering the potential to reduce childhood developmental inequities at scale.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".