The Changing Geographies of Childhood in Southeast Asia: Narrative Maps from Three Generations in Hanoi and Yogyakarta
Bibliographic record
Abstract
This article investigates the intergenerational changes in children’s play and mobility spaces in two rapidly urbanising Southeast Asian cities, Hanoi and Yogyakarta. Drawing from 22 intergenerational case studies, we use narrative mapping to highlight important trends and discrepancies. In both cities, important contractions in the geographical scope and communal nature of play and mobility have taken place over three generations, driven by factors such as changes in urban land-use, safety concerns, and lifestyles. In Hanoi, grandparents’ childhoods were characterised by expansive, communal outdoor play areas, in stark contrast to today’s confined, indoor spaces. Similarly, in Yogyakarta, children’s play spaces have become increasingly privatised and controlled. Despite subtle differences in causes and patterns, our study reveals common urban trajectories across different political systems. This highlights the pervasive influence of neo-liberalism, urban expansion, and socio-cultural shifts on childhood experiences in Asian cities. These findings have critical implications for children’s health, social development, and community belonging, and emphasise the urgent need for urban planners and policymakers in Southeast Asia to integrate child-friendly initiatives in urban development to foster healthier, more inclusive cities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".