Affordances of Home-School Journey in the Hinterland Area for Spatial Mobility Patterns
Bibliographic record
Abstract
In children's home-school journey, little is known about the geography of the outdoor environment in coastal areas, particularly in the hinterland area.This study investigates spatial mobility patterns during home-school journeys in the hinterland area through their actualised affordances at Belakang Padang, Batam, Indonesia.In this study, affordances refer to the opportunities and constraints related to physical and environmental factors that influence a child's ability to navigate and move within their surroundings, particularly when travelling to and from school.The study was conducted on forty-three children aged 7-12 who experienced two elementary schools in the hinterland of an island community.The phenomenological approach elicited a dataset of the children's behavioural responses derived from participatory observation, go-along interviews, and GIS mapping.The responses included physical movement, words, and phrases, which suggested their preferences towards the hinterland area settings.This study analysed the data in two stages: firstly, a taxonomy of the affordance of children's outdoor environment involves categorising different types of opportunities and constraints that impact spatial mobility patterns, and secondly, the level of affordances shows how the perceiver shapes and constructs the environment they are perceiving.Spatial analysis and content analyses revealed that the home-school journey offered 11 categories of environmental qualities, with the category of water showing the most affordances.The children's activities were most frequently on the jetty and muddy ground.The results suggest that children's affordances such as observing, recognising, watching, relaxing, scooping, swimming, diving, playing, speaking, paddling, practising, crossing, standing, touching, eating, floating, fishing, stopping, explaining, releasing, mastering, catching, asking, joking, telling, singing, planning, sitting, splashing, pouring, determining, knowing, boating, and standing to gain balance on the jetty and boat are facilitated through physical, cognitive, and social interactions.A deeper understanding of child-friendly places can help increase the affordance of other places in the hinterland area.Therefore, it is recommended that governments implement policies to foster a Child-Friendly City/District (CFC/D).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".